1_Introduccion.ipynb

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    "colab": {
      "name": "OLS Departamentos AMBA.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rpasquini/metodos_cuantitativos/blob/main/1_Introduccion.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2pjrWWN2Hq6x"
      },
      "source": [
        "Autor: Ricardo Pasquini 2024\n",
        "rpasquini@utdt.edu\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3dudLE3CrKxs"
      },
      "source": [
        "# Introducción\n",
        "En este notebook introduciremos algunas de las principales librerías con las que vamos a estar trabajando. Nuestro objetivo inicial es estimar un modelo de regresión simple utilizando datos de alquileres de 2019.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8jStxxotyK78"
      },
      "source": [
        "## Donde estamos\n",
        "\n",
        "Nuestro ambiente de trabajo ahora es un *notebook* que permite correr codigo, procesar datos, imprimir gráficos, etc. Estos notebooks en general se conocen como \"Jupyter notebooks\" y tienen como particularidad que el  entorno visual en el que trabajan vive en el navegador. En este momento además, el notebook con el que trabajan \"vive en la nube\" de Google Colaboratory. Utiliza recursos de la nube de Google para procesar los datos, etc. Para trabajar con nuestros propios datos tendremos que hacerlos accesibles a Google Colab (subiendo los datos al Colab o subiendo los datos a otro servidor al cual las librerias que usemos puedan acceder)\n",
        "\n",
        "Comentarios relacionados:\n",
        "\n",
        "- Como dijimos el notebook es solo un entorno visual. En este notebook vamos a trabajar con librerias de análisis cuantitativo construidas sobre el lenguaje Python. Los notebooks de Colab tambien pueden correr código de R.\n",
        "- Trabajar en la nube \"Colab\" es conveniente para una clase. También es posible (y a veces necesario) trabajar con un Jupyter Notebook instalado \"localmente\" en sus computadoras.\n",
        "\n",
        "- El ambiente virtual provisto por Google Colab va a borrar todo el contenido que se aloje en la memoria virtual después de un rato de inactividad. Lo unico que se mantiene es lo que está escrito en el notebook (el codigo y el texto explicativo). Esto implica que el notebook debe contener todo el codigo desde la carga de los datos hasta su resultado final para que pueda ser reproducido en cualquier momento.  \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lwDlB9JL1B27"
      },
      "source": [
        "# Librerías con las que vamos a trabajar\n",
        "\n",
        "Vamos a arrancar con estas:\n",
        "\n",
        "- Pandas: Una de las principales librerías para trabajar y manipular datos en Python en forma tabular (llamados **dataframes** y usualmente abreviados **df**)\n",
        "- Geopandas: Una libreria que busca ser similar a Pandas pero adaptada adicionalmente a  datos con una dimensión geográfica. A los objetos de geopandas los llamamos **geodataframes**.\n",
        "- StatsModels: Una librería para procesamientos estadísticos.\n",
        "\n",
        "Al respecto del uso de librerías, además debemos tener en cuenta:\n",
        "\n",
        "- En general para usar las librerías tienen que estar *instaladas* y además *importadas * al ambiente de trabajo en el que estemos.\n",
        "- Los Notebooks de Colab vienen con algunas librerías preinstaladas. Solo será necesario importarlas. Para las que no, será necesario instalarlas ( usando un comando de instalacion llamado *pip*).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IolxcRA36t_w"
      },
      "source": [
        "## Instalamos e importamos geopandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gx11uGlqHlXN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f4de14e-71bd-430f-f220-5c757f545ae8"
      },
      "source": [
        "#!pip install geopandas==0.9.0\n",
        "!pip install geopandas"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: geopandas in /usr/local/lib/python3.10/dist-packages (0.9.0)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from geopandas) (2.0.3)\n",
            "Requirement already satisfied: shapely>=1.6 in /usr/local/lib/python3.10/dist-packages (from geopandas) (2.0.4)\n",
            "Requirement already satisfied: fiona>=1.8 in /usr/local/lib/python3.10/dist-packages (from geopandas) (1.9.6)\n",
            "Requirement already satisfied: pyproj>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from geopandas) (3.6.1)\n",
            "Requirement already satisfied: attrs>=19.2.0 in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8->geopandas) (23.2.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8->geopandas) (2024.6.2)\n",
            "Requirement already satisfied: click~=8.0 in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8->geopandas) (8.1.7)\n",
            "Requirement already satisfied: click-plugins>=1.0 in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8->geopandas) (1.1.1)\n",
            "Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8->geopandas) (0.7.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8->geopandas) (1.16.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.0->geopandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.0->geopandas) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.0->geopandas) (2024.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.0->geopandas) (1.25.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gLBowd4z63WV"
      },
      "source": [
        "Ahora importamos geopandas. Una vez importada, la libreria en cuestion vive en el nombre de la variable que lleva su nombre. Podemos ademas cambiar ese nombre al momento de la importacion usando ***as***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ILipZ9mqH1pQ"
      },
      "source": [
        "import geopandas as gpd"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jgAYUIpjHaKu"
      },
      "source": [
        "La fuente de los datos es Properati [properati.com.ar/data/\n",
        "](https://properati.com.ar/data/).\n",
        "Fue descargada haciendo un pedido a su base de datos disponible en Google Big Query\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BPo1_6_X7dlj"
      },
      "source": [
        "El método GeoDataFrame.from_file me permite levantar datos desde un archivo y convertirlos en un **GeoDataFrame**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qFmIW55RH73q"
      },
      "source": [
        "departamentos=gpd.GeoDataFrame.from_file(\"https://github.com/rpasquini/urban-econometrics/blob/master/data/departamentos2019amba.zip?raw=true\")"
      ],
      "execution_count": 3,
      "outputs": []
    },
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        "outputId": "28d274af-3198-45c1-f213-636b14dcb035"
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      "source": [
        "departamentos"
      ],
      "execution_count": 4,
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              "       bathrooms  bedrooms    country  created_on currency  \\\n",
              "0            1.0       1.0  Argentina  2019-09-26      ARS   \n",
              "1            3.0       NaN  Argentina  2019-09-26      ARS   \n",
              "2            1.0       2.0  Argentina  2019-09-26      ARS   \n",
              "3            NaN       NaN  Argentina  2019-09-26      ARS   \n",
              "4            1.0       1.0  Argentina  2019-09-26      ARS   \n",
              "...          ...       ...        ...         ...      ...   \n",
              "50648        1.0       1.0  Argentina  2019-02-27      ARS   \n",
              "50649        1.0       NaN  Argentina  2019-02-27      ARS   \n",
              "50650        2.0       4.0  Argentina  2019-02-27      ARS   \n",
              "50651        1.0       1.0  Argentina  2019-02-27      ARS   \n",
              "50652        1.0       2.0  Argentina  2019-02-27      ARS   \n",
              "\n",
              "                                              descriptio developmen  \\\n",
              "0      CODIGO: 1819-CRI180 ubicado en: Av. Callao 180...       None   \n",
              "1      Residencia de estudiantes y viajeros. Son dos ...       None   \n",
              "2      CODIGO: 1348-AF021227 ubicado en: Tacuari 263 ...       None   \n",
              "3      Departamento de dos ambientes a 200 metros de ...       None   \n",
              "4      CODIGO: 733-1600MARIANOA ubicado en: MARIANO A...       None   \n",
              "...                                                  ...        ...   \n",
              "50648  Corredor Responsable: Martin Macagno - CUCICBA...       None   \n",
              "50649  **** ALQUILER DUEÑO DIRECTO DE DEPARTAMENTO DE...       None   \n",
              "50650  Corredor Responsable: JAVIER HERNANDEZ - CUCIC...       None   \n",
              "50651  Xintel (DIP-DIP-632) EN ALQUILER DEPARTAMENTO ...       None   \n",
              "50652  Xintel (ROC-ROC-1972) <p>Departamento 3 AMBIEN...       None   \n",
              "\n",
              "         end_date  index         l1  ...  price price_peri rooms  start_date  \\\n",
              "0      2019-10-10      0  Argentina  ...  18000    Mensual   NaN  2019-09-26   \n",
              "1      2019-11-04      1  Argentina  ...  45000    Mensual   NaN  2019-09-26   \n",
              "2      2019-09-28      2  Argentina  ...  18500    Mensual   NaN  2019-09-26   \n",
              "3      2020-02-12      4  Argentina  ...  11000    Mensual   NaN  2019-09-26   \n",
              "4      2019-11-10      5  Argentina  ...  18500    Mensual   NaN  2019-09-26   \n",
              "...           ...    ...        ...  ...    ...        ...   ...         ...   \n",
              "50648  2019-04-24  60856  Argentina  ...  17500    Mensual   2.0  2019-02-27   \n",
              "50649  2019-03-29  60857  Argentina  ...  11000    Mensual   NaN  2019-02-27   \n",
              "50650  2019-03-10  60858  Argentina  ...  40000    Mensual   5.0  2019-02-27   \n",
              "50651  2019-06-25  60859  Argentina  ...   4500    Mensual   2.0  2019-02-27   \n",
              "50652  2019-06-18  60860  Argentina  ...  13000    Mensual   3.0  2019-02-27   \n",
              "\n",
              "      surface_co  surface_to  \\\n",
              "0             52          58   \n",
              "1            219         219   \n",
              "2             61          67   \n",
              "3             38          38   \n",
              "4             39          49   \n",
              "...          ...         ...   \n",
              "50648         45          45   \n",
              "50649         43          49   \n",
              "50650        150         150   \n",
              "50651         40          40   \n",
              "50652         57          57   \n",
              "\n",
              "                                                   title          type  \\\n",
              "0      Recoleta. Av. Callao y Av. Alvear. Piso alto. ...  Departamento   \n",
              "1                               Departamento - Balvanera  Departamento   \n",
              "2      3 ambientes con patio 61 m2 propios bajas expe...  Departamento   \n",
              "3                               Departamento - San Justo  Departamento   \n",
              "4      EXCELENTE DEPARTAMENTO 2 AMB CON PATIO 49 MTS ...  Departamento   \n",
              "...                                                  ...           ...   \n",
              "50648  ALQUILER DEPARTAMENTO 2 AMB A ESTRENAR P CHACA...  Departamento   \n",
              "50649  Carlos A Lopez  2700 1°  - $ 11.000 - Departam...  Departamento   \n",
              "50650        Alquiler-Triplex c/ cochera - sin expensas.  Departamento   \n",
              "50651                           DEPARTAMENTO EN ALQUILER  Departamento   \n",
              "50652                           DEPARTAMENTO EN ALQUILER  Departamento   \n",
              "\n",
              "          type_i18n                       geometry  \n",
              "0      Departamento  POINT (106827.294 104558.628)  \n",
              "1      Departamento  POINT (106347.880 102524.692)  \n",
              "2      Departamento  POINT (107658.254 102034.095)  \n",
              "3      Departamento    POINT (91709.240 94331.755)  \n",
              "4      Departamento   POINT (99129.692 105739.092)  \n",
              "...             ...                            ...  \n",
              "50648  Departamento  POINT (101961.679 100016.721)  \n",
              "50649  Departamento   POINT (96460.074 105041.354)  \n",
              "50650  Departamento   POINT (95515.687 105731.369)  \n",
              "50651  Departamento   POINT (90520.492 106346.015)  \n",
              "50652  Departamento   POINT (92158.251 108684.417)  \n",
              "\n",
              "[50653 rows x 29 columns]"
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              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50648</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>2019-02-27</td>\n",
              "      <td>ARS</td>\n",
              "      <td>Corredor Responsable: Martin Macagno - CUCICBA...</td>\n",
              "      <td>None</td>\n",
              "      <td>2019-04-24</td>\n",
              "      <td>60856</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>...</td>\n",
              "      <td>17500</td>\n",
              "      <td>Mensual</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2019-02-27</td>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "      <td>ALQUILER DEPARTAMENTO 2 AMB A ESTRENAR P CHACA...</td>\n",
              "      <td>Departamento</td>\n",
              "      <td>Departamento</td>\n",
              "      <td>POINT (101961.679 100016.721)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50649</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>2019-02-27</td>\n",
              "      <td>ARS</td>\n",
              "      <td>**** ALQUILER DUEÑO DIRECTO DE DEPARTAMENTO DE...</td>\n",
              "      <td>None</td>\n",
              "      <td>2019-03-29</td>\n",
              "      <td>60857</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>...</td>\n",
              "      <td>11000</td>\n",
              "      <td>Mensual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-02-27</td>\n",
              "      <td>43</td>\n",
              "      <td>49</td>\n",
              "      <td>Carlos A Lopez  2700 1°  - $ 11.000 - Departam...</td>\n",
              "      <td>Departamento</td>\n",
              "      <td>Departamento</td>\n",
              "      <td>POINT (96460.074 105041.354)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50650</th>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>2019-02-27</td>\n",
              "      <td>ARS</td>\n",
              "      <td>Corredor Responsable: JAVIER HERNANDEZ - CUCIC...</td>\n",
              "      <td>None</td>\n",
              "      <td>2019-03-10</td>\n",
              "      <td>60858</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>...</td>\n",
              "      <td>40000</td>\n",
              "      <td>Mensual</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2019-02-27</td>\n",
              "      <td>150</td>\n",
              "      <td>150</td>\n",
              "      <td>Alquiler-Triplex c/ cochera - sin expensas.</td>\n",
              "      <td>Departamento</td>\n",
              "      <td>Departamento</td>\n",
              "      <td>POINT (95515.687 105731.369)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50651</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>2019-02-27</td>\n",
              "      <td>ARS</td>\n",
              "      <td>Xintel (DIP-DIP-632) EN ALQUILER DEPARTAMENTO ...</td>\n",
              "      <td>None</td>\n",
              "      <td>2019-06-25</td>\n",
              "      <td>60859</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>...</td>\n",
              "      <td>4500</td>\n",
              "      <td>Mensual</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2019-02-27</td>\n",
              "      <td>40</td>\n",
              "      <td>40</td>\n",
              "      <td>DEPARTAMENTO EN ALQUILER</td>\n",
              "      <td>Departamento</td>\n",
              "      <td>Departamento</td>\n",
              "      <td>POINT (90520.492 106346.015)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50652</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>2019-02-27</td>\n",
              "      <td>ARS</td>\n",
              "      <td>Xintel (ROC-ROC-1972) &lt;p&gt;Departamento 3 AMBIEN...</td>\n",
              "      <td>None</td>\n",
              "      <td>2019-06-18</td>\n",
              "      <td>60860</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>...</td>\n",
              "      <td>13000</td>\n",
              "      <td>Mensual</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2019-02-27</td>\n",
              "      <td>57</td>\n",
              "      <td>57</td>\n",
              "      <td>DEPARTAMENTO EN ALQUILER</td>\n",
              "      <td>Departamento</td>\n",
              "      <td>Departamento</td>\n",
              "      <td>POINT (92158.251 108684.417)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>50653 rows × 29 columns</p>\n",
              "</div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "departamentos"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GSgPw3sDjnUq"
      },
      "source": [
        "# Si cargan desde el ambiente de Colab:\n",
        "#departamentos=gpd.read_file(\"zip://departamentos.zip!departamentos.shp\")"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rUsH8R-OItXn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "outputId": "ee218bac-7b7a-45d6-a32f-fa45e960ba0d"
      },
      "source": [
        "departamentos.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   bathrooms  bedrooms    country  created_on currency  \\\n",
              "0        1.0       1.0  Argentina  2019-09-26      ARS   \n",
              "1        3.0       NaN  Argentina  2019-09-26      ARS   \n",
              "2        1.0       2.0  Argentina  2019-09-26      ARS   \n",
              "3        NaN       NaN  Argentina  2019-09-26      ARS   \n",
              "4        1.0       1.0  Argentina  2019-09-26      ARS   \n",
              "\n",
              "                                          descriptio developmen    end_date  \\\n",
              "0  CODIGO: 1819-CRI180 ubicado en: Av. Callao 180...       None  2019-10-10   \n",
              "1  Residencia de estudiantes y viajeros. Son dos ...       None  2019-11-04   \n",
              "2  CODIGO: 1348-AF021227 ubicado en: Tacuari 263 ...       None  2019-09-28   \n",
              "3  Departamento de dos ambientes a 200 metros de ...       None  2020-02-12   \n",
              "4  CODIGO: 733-1600MARIANOA ubicado en: MARIANO A...       None  2019-11-10   \n",
              "\n",
              "   index         l1  ...  price price_peri rooms  start_date surface_co  \\\n",
              "0      0  Argentina  ...  18000    Mensual   NaN  2019-09-26         52   \n",
              "1      1  Argentina  ...  45000    Mensual   NaN  2019-09-26        219   \n",
              "2      2  Argentina  ...  18500    Mensual   NaN  2019-09-26         61   \n",
              "3      4  Argentina  ...  11000    Mensual   NaN  2019-09-26         38   \n",
              "4      5  Argentina  ...  18500    Mensual   NaN  2019-09-26         39   \n",
              "\n",
              "   surface_to                                              title  \\\n",
              "0          58  Recoleta. Av. Callao y Av. Alvear. Piso alto. ...   \n",
              "1         219                           Departamento - Balvanera   \n",
              "2          67  3 ambientes con patio 61 m2 propios bajas expe...   \n",
              "3          38                           Departamento - San Justo   \n",
              "4          49  EXCELENTE DEPARTAMENTO 2 AMB CON PATIO 49 MTS ...   \n",
              "\n",
              "           type     type_i18n                       geometry  \n",
              "0  Departamento  Departamento  POINT (106827.294 104558.628)  \n",
              "1  Departamento  Departamento  POINT (106347.880 102524.692)  \n",
              "2  Departamento  Departamento  POINT (107658.254 102034.095)  \n",
              "3  Departamento  Departamento    POINT (91709.240 94331.755)  \n",
              "4  Departamento  Departamento   POINT (99129.692 105739.092)  \n",
              "\n",
              "[5 rows x 29 columns]"
            ],
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>country</th>\n",
              "      <th>created_on</th>\n",
              "      <th>currency</th>\n",
              "      <th>descriptio</th>\n",
              "      <th>developmen</th>\n",
              "      <th>end_date</th>\n",
              "      <th>index</th>\n",
              "      <th>l1</th>\n",
              "      <th>...</th>\n",
              "      <th>price</th>\n",
              "      <th>price_peri</th>\n",
              "      <th>rooms</th>\n",
              "      <th>start_date</th>\n",
              "      <th>surface_co</th>\n",
              "      <th>surface_to</th>\n",
              "      <th>title</th>\n",
              "      <th>type</th>\n",
              "      <th>type_i18n</th>\n",
              "      <th>geometry</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>2019-09-26</td>\n",
              "      <td>ARS</td>\n",
              "      <td>CODIGO: 1819-CRI180 ubicado en: Av. Callao 180...</td>\n",
              "      <td>None</td>\n",
              "      <td>2019-10-10</td>\n",
              "      <td>0</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>...</td>\n",
              "      <td>18000</td>\n",
              "      <td>Mensual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-09-26</td>\n",
              "      <td>52</td>\n",
              "      <td>58</td>\n",
              "      <td>Recoleta. Av. Callao y Av. Alvear. Piso alto. ...</td>\n",
              "      <td>Departamento</td>\n",
              "      <td>Departamento</td>\n",
              "      <td>POINT (106827.294 104558.628)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>2019-09-26</td>\n",
              "      <td>ARS</td>\n",
              "      <td>Residencia de estudiantes y viajeros. Son dos ...</td>\n",
              "      <td>None</td>\n",
              "      <td>2019-11-04</td>\n",
              "      <td>1</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>...</td>\n",
              "      <td>45000</td>\n",
              "      <td>Mensual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-09-26</td>\n",
              "      <td>219</td>\n",
              "      <td>219</td>\n",
              "      <td>Departamento - Balvanera</td>\n",
              "      <td>Departamento</td>\n",
              "      <td>Departamento</td>\n",
              "      <td>POINT (106347.880 102524.692)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>2019-09-26</td>\n",
              "      <td>ARS</td>\n",
              "      <td>CODIGO: 1348-AF021227 ubicado en: Tacuari 263 ...</td>\n",
              "      <td>None</td>\n",
              "      <td>2019-09-28</td>\n",
              "      <td>2</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>...</td>\n",
              "      <td>18500</td>\n",
              "      <td>Mensual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-09-26</td>\n",
              "      <td>61</td>\n",
              "      <td>67</td>\n",
              "      <td>3 ambientes con patio 61 m2 propios bajas expe...</td>\n",
              "      <td>Departamento</td>\n",
              "      <td>Departamento</td>\n",
              "      <td>POINT (107658.254 102034.095)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>2019-09-26</td>\n",
              "      <td>ARS</td>\n",
              "      <td>Departamento de dos ambientes a 200 metros de ...</td>\n",
              "      <td>None</td>\n",
              "      <td>2020-02-12</td>\n",
              "      <td>4</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>...</td>\n",
              "      <td>11000</td>\n",
              "      <td>Mensual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-09-26</td>\n",
              "      <td>38</td>\n",
              "      <td>38</td>\n",
              "      <td>Departamento - San Justo</td>\n",
              "      <td>Departamento</td>\n",
              "      <td>Departamento</td>\n",
              "      <td>POINT (91709.240 94331.755)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>2019-09-26</td>\n",
              "      <td>ARS</td>\n",
              "      <td>CODIGO: 733-1600MARIANOA ubicado en: MARIANO A...</td>\n",
              "      <td>None</td>\n",
              "      <td>2019-11-10</td>\n",
              "      <td>5</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>...</td>\n",
              "      <td>18500</td>\n",
              "      <td>Mensual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-09-26</td>\n",
              "      <td>39</td>\n",
              "      <td>49</td>\n",
              "      <td>EXCELENTE DEPARTAMENTO 2 AMB CON PATIO 49 MTS ...</td>\n",
              "      <td>Departamento</td>\n",
              "      <td>Departamento</td>\n",
              "      <td>POINT (99129.692 105739.092)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 29 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-282464e7-8c92-4af0-917c-999020e2fc06')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-282464e7-8c92-4af0-917c-999020e2fc06 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-282464e7-8c92-4af0-917c-999020e2fc06');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7de28c79-de26-4e75-aee5-bd119f11ba72\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7de28c79-de26-4e75-aee5-bd119f11ba72')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7de28c79-de26-4e75-aee5-bd119f11ba72 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "departamentos"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ec6lqKxK_Bm"
      },
      "source": [
        "## Descripcion rápida de los datos\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xlUxmaSPy2ar",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "997e97cc-dc94-4ecc-8bac-9a312f1a3628"
      },
      "source": [
        "departamentos.columns"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['bathrooms', 'bedrooms', 'country', 'created_on', 'currency',\n",
              "       'descriptio', 'developmen', 'end_date', 'index', 'l1', 'l2', 'l3', 'l4',\n",
              "       'l5', 'l6', 'lat', 'lon', 'operation', 'operation_', 'price',\n",
              "       'price_peri', 'rooms', 'start_date', 'surface_co', 'surface_to',\n",
              "       'title', 'type', 'type_i18n', 'geometry'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s09R5VxOzLJd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50ef9f21-5222-46d7-f1fe-6ddcc97aa9fa"
      },
      "source": [
        "departamentos.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50653, 29)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dz2svI6Ul1Lk"
      },
      "source": [
        "Puedo llamar a una columna en particular del dataframe si la escribo entre corchetes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTvv8JVw-m1p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d84cf190-9065-4745-e90c-43cef7ff9b9f"
      },
      "source": [
        "departamentos['l2']"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                 Capital Federal\n",
              "1                 Capital Federal\n",
              "2                 Capital Federal\n",
              "3        Bs.As. G.B.A. Zona Oeste\n",
              "4                 Capital Federal\n",
              "                   ...           \n",
              "50648             Capital Federal\n",
              "50649             Capital Federal\n",
              "50650             Capital Federal\n",
              "50651    Bs.As. G.B.A. Zona Norte\n",
              "50652    Bs.As. G.B.A. Zona Norte\n",
              "Name: l2, Length: 50653, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rM5MXRBHmdyr"
      },
      "source": [
        "En algunos casos funcionará que solo escriba . y el nombre de la columna. (esto no sirve, por ejemplo si las columnas  tienen espacios en el nombre)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3MKDeQk6mbJu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84c37c36-f684-4d22-ac29-bc0dbb5d8fe0"
      },
      "source": [
        "departamentos.l2"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                 Capital Federal\n",
              "1                 Capital Federal\n",
              "2                 Capital Federal\n",
              "3        Bs.As. G.B.A. Zona Oeste\n",
              "4                 Capital Federal\n",
              "                   ...           \n",
              "50648             Capital Federal\n",
              "50649             Capital Federal\n",
              "50650             Capital Federal\n",
              "50651    Bs.As. G.B.A. Zona Norte\n",
              "50652    Bs.As. G.B.A. Zona Norte\n",
              "Name: l2, Length: 50653, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RCqE-TV8myVO"
      },
      "source": [
        "Luego puedo ver cuales son los valores unicos que toma la variable:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KvdYZ5gjmEbV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd643140-4061-4122-cfee-da7d8ffadd08"
      },
      "source": [
        "departamentos['l2'].unique()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Capital Federal', 'Bs.As. G.B.A. Zona Oeste',\n",
              "       'Bs.As. G.B.A. Zona Sur', 'Bs.As. G.B.A. Zona Norte',\n",
              "       'Buenos Aires Interior'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vn5yGYl_zdAR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6dfae3c3-23ae-4cd8-d4b0-d07472caa179"
      },
      "source": [
        "departamentos.dtypes"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "bathrooms      float64\n",
              "bedrooms       float64\n",
              "country         object\n",
              "created_on      object\n",
              "currency        object\n",
              "descriptio      object\n",
              "developmen      object\n",
              "end_date        object\n",
              "index            int64\n",
              "l1              object\n",
              "l2              object\n",
              "l3              object\n",
              "l4              object\n",
              "l5              object\n",
              "l6              object\n",
              "lat            float64\n",
              "lon            float64\n",
              "operation       object\n",
              "operation_      object\n",
              "price            int64\n",
              "price_peri      object\n",
              "rooms          float64\n",
              "start_date      object\n",
              "surface_co       int64\n",
              "surface_to       int64\n",
              "title           object\n",
              "type            object\n",
              "type_i18n       object\n",
              "geometry      geometry\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AsY9Zz1Y08E0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "7d34e4e6-c0ad-463c-f8e8-00448790ca78"
      },
      "source": [
        "departamentos.plot()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qmopF4V8KR4A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3da9db13-0eb7-4af9-8bdb-8d5a46e25197"
      },
      "source": [
        "departamentos.price.describe()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    5.065300e+04\n",
              "mean     1.780301e+04\n",
              "std      1.713533e+04\n",
              "min      5.000000e+01\n",
              "25%      1.050000e+04\n",
              "50%      1.500000e+04\n",
              "75%      2.100000e+04\n",
              "max      1.500000e+06\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fx9f6Bta4FPD"
      },
      "source": [
        "## Estudiando un subconjunto de datos (filas del dataframe)\n",
        "\n",
        "Para \"filtrar\" el dataframe usamos el método .loc, especificando entre corchetes la condición que filtra nuestros datos\n",
        "\n",
        "Por ejemplo, si queremos departamentos mayores a $100000\n",
        "departamentos.loc[(departamentos.price>100000)]\n",
        "\n",
        "Para escribir una condicion recuerden que pueden utilizar los siguientes lógicos:\n",
        "\n",
        "```==    para chequear la  condicion \"equivale a\" o \"igual a\" va doble igual  ```\n",
        "\n",
        "```!= distinto a ```\n",
        "\n",
        "```> mayor a  ```\n",
        "\n",
        "```>= mayor o igual a ```\n",
        "\n",
        "```< menor a ```\n",
        "\n",
        "```<= menor o igual a ```\n",
        "\n",
        "En el caso de querer usar dos condiciones o más:\n",
        "\n",
        "```(esta condicion) & (esta condicion) ``` Se cumple esta condicion Y esta otra  \n",
        "\n",
        "```(esta condicion) | (esta condicion) ``` Se cumple esta condicion O esta otra\n",
        "   \n",
        "\n",
        "Recuerden que las condiciones tiene que ir  en parentesis!\n",
        "\n",
        "\n",
        "Vean los siguientes ejemplos:\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XFZnxWQllCn8"
      },
      "source": [
        "\n",
        "\n",
        "Filtrar los departamentos que estan en Capital Federal\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l5LnPCc_lALA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 929
        },
        "outputId": "e2ece2f4-5274-4425-82e3-4d14b1b3f7b9"
      },
      "source": [
        "departamentos.loc[departamentos['l2']==\"Capital Federal\"]"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       bathrooms  bedrooms    country  created_on currency  \\\n",
              "0            1.0       1.0  Argentina  2019-09-26      ARS   \n",
              "1            3.0       NaN  Argentina  2019-09-26      ARS   \n",
              "2            1.0       2.0  Argentina  2019-09-26      ARS   \n",
              "4            1.0       1.0  Argentina  2019-09-26      ARS   \n",
              "5            NaN       NaN  Argentina  2019-09-26      ARS   \n",
              "...          ...       ...        ...         ...      ...   \n",
              "50638        1.0       NaN  Argentina  2019-02-27      ARS   \n",
              "50639        1.0       1.0  Argentina  2019-02-27      ARS   \n",
              "50648        1.0       1.0  Argentina  2019-02-27      ARS   \n",
              "50649        1.0       NaN  Argentina  2019-02-27      ARS   \n",
              "50650        2.0       4.0  Argentina  2019-02-27      ARS   \n",
              "\n",
              "                                              descriptio developmen  \\\n",
              "0      CODIGO: 1819-CRI180 ubicado en: Av. Callao 180...       None   \n",
              "1      Residencia de estudiantes y viajeros. Son dos ...       None   \n",
              "2      CODIGO: 1348-AF021227 ubicado en: Tacuari 263 ...       None   \n",
              "4      CODIGO: 733-1600MARIANOA ubicado en: MARIANO A...       None   \n",
              "5      MONOAMBIENTE EN ALMAGRO<br>AMPLIO Y LUMINOSO<b...       None   \n",
              "...                                                  ...        ...   \n",
              "50638  ALQUILER EXCELENTE MONOAMBIENTE MUY LUMINOSO A...       None   \n",
              "50639  Monoambiente de 35m2 Totales en complejo Green...       None   \n",
              "50648  Corredor Responsable: Martin Macagno - CUCICBA...       None   \n",
              "50649  **** ALQUILER DUEÑO DIRECTO DE DEPARTAMENTO DE...       None   \n",
              "50650  Corredor Responsable: JAVIER HERNANDEZ - CUCIC...       None   \n",
              "\n",
              "         end_date  index         l1  ...  price price_peri rooms  start_date  \\\n",
              "0      2019-10-10      0  Argentina  ...  18000    Mensual   NaN  2019-09-26   \n",
              "1      2019-11-04      1  Argentina  ...  45000    Mensual   NaN  2019-09-26   \n",
              "2      2019-09-28      2  Argentina  ...  18500    Mensual   NaN  2019-09-26   \n",
              "4      2019-11-10      5  Argentina  ...  18500    Mensual   NaN  2019-09-26   \n",
              "5      2019-10-02      6  Argentina  ...   9000    Mensual   1.0  2019-09-26   \n",
              "...           ...    ...        ...  ...    ...        ...   ...         ...   \n",
              "50638  2019-03-16  60841  Argentina  ...  12500    Mensual   1.0  2019-02-27   \n",
              "50639  2019-07-17  60842  Argentina  ...  15000       None   1.0  2019-02-27   \n",
              "50648  2019-04-24  60856  Argentina  ...  17500    Mensual   2.0  2019-02-27   \n",
              "50649  2019-03-29  60857  Argentina  ...  11000    Mensual   NaN  2019-02-27   \n",
              "50650  2019-03-10  60858  Argentina  ...  40000    Mensual   5.0  2019-02-27   \n",
              "\n",
              "      surface_co  surface_to  \\\n",
              "0             52          58   \n",
              "1            219         219   \n",
              "2             61          67   \n",
              "4             39          49   \n",
              "5             30          30   \n",
              "...          ...         ...   \n",
              "50638         32          37   \n",
              "50639         27          32   \n",
              "50648         45          45   \n",
              "50649         43          49   \n",
              "50650        150         150   \n",
              "\n",
              "                                                   title          type  \\\n",
              "0      Recoleta. Av. Callao y Av. Alvear. Piso alto. ...  Departamento   \n",
              "1                               Departamento - Balvanera  Departamento   \n",
              "2      3 ambientes con patio 61 m2 propios bajas expe...  Departamento   \n",
              "4      EXCELENTE DEPARTAMENTO 2 AMB CON PATIO 49 MTS ...  Departamento   \n",
              "5                                 Departamento - Almagro  Departamento   \n",
              "...                                                  ...           ...   \n",
              "50638  ALQUILER EXCELENTE MONOAMBIENTE MUY LUMINOSO A...  Departamento   \n",
              "50639     Excelente departamento en Alquiler con cochera  Departamento   \n",
              "50648  ALQUILER DEPARTAMENTO 2 AMB A ESTRENAR P CHACA...  Departamento   \n",
              "50649  Carlos A Lopez  2700 1°  - $ 11.000 - Departam...  Departamento   \n",
              "50650        Alquiler-Triplex c/ cochera - sin expensas.  Departamento   \n",
              "\n",
              "          type_i18n                       geometry  \n",
              "0      Departamento  POINT (106827.294 104558.628)  \n",
              "1      Departamento  POINT (106347.880 102524.692)  \n",
              "2      Departamento  POINT (107658.254 102034.095)  \n",
              "4      Departamento   POINT (99129.692 105739.092)  \n",
              "5      Departamento  POINT (103825.371 102026.223)  \n",
              "...             ...                            ...  \n",
              "50638  Departamento   POINT (99005.286 106249.504)  \n",
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              "      <td>60856</td>\n",
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              "      <td>60858</td>\n",
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              "      <td>Departamento</td>\n",
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          "metadata": {},
          "execution_count": 15
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vULG9Ru_kmKd"
      },
      "source": [
        "Filtrar los departamentos que valen más de $100000 Y que además estan en capital federal"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s57f-ms12k2D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4992d2e7-a164-4033-e350-a8e41b40adbe"
      },
      "source": [
        "departamentos.loc[(departamentos.price>100000)&(departamentos.l2==\"Capital Federal\")]"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       bathrooms  bedrooms    country  created_on currency  \\\n",
              "200          3.0       NaN  Argentina  2019-12-20      ARS   \n",
              "985          NaN       NaN  Argentina  2019-04-15      ARS   \n",
              "1346         1.0       1.0  Argentina  2019-01-31      ARS   \n",
              "1621         2.0       3.0  Argentina  2019-03-08      ARS   \n",
              "2260         2.0       NaN  Argentina  2019-05-09      ARS   \n",
              "...          ...       ...        ...         ...      ...   \n",
              "48649        3.0       3.0  Argentina  2019-07-31      ARS   \n",
              "48742        4.0       3.0  Argentina  2019-07-31      ARS   \n",
              "49168        3.0       4.0  Argentina  2019-07-31      ARS   \n",
              "49795        3.0       3.0  Argentina  2019-08-01      ARS   \n",
              "49852        2.0       3.0  Argentina  2019-08-01      ARS   \n",
              "\n",
              "                                              descriptio developmen  \\\n",
              "200    *** A ESTRENAR ***<br><br>Espectacular 4 amb. ...       None   \n",
              "985                                  test test test test       None   \n",
              "1346   Corredor Responsable: JAVIER HERNANDEZ - CUCIC...       None   \n",
              "1621   ALQUILER 4 AMBIENTES EN PALERMO SIN MUEBLES EN...       None   \n",
              "2260   PISO EN EDIFICIO DE ESTILO FRANCÉS DE 12 PISOS...       None   \n",
              "...                                                  ...        ...   \n",
              "48649  Departamento único en piso altísimo con todos ...       None   \n",
              "48742  Alquiler Departamento tipo duplex. Rodea de un...       None   \n",
              "49168  Semipiso 5 amb c/dependencia y 2 cocheras cubi...       None   \n",
              "49795  Planta de categoría en el Palacio Bellini! El ...       None   \n",
              "49852  Exclusivo duplex con vista abierta  al río, li...       None   \n",
              "\n",
              "         end_date  index         l1  ...    price price_peri rooms  \\\n",
              "200    2020-05-14    255  Argentina  ...   160000    Mensual   4.0   \n",
              "985    2019-07-26   1210  Argentina  ...  1232134    Mensual   1.0   \n",
              "1346   2019-08-07   1662  Argentina  ...   120000       None   2.0   \n",
              "1621   2019-04-30   1983  Argentina  ...   139000    Mensual   4.0   \n",
              "2260   2019-06-14   2757  Argentina  ...   105000    Mensual   4.0   \n",
              "...           ...    ...        ...  ...      ...        ...   ...   \n",
              "48649  2019-08-14  58561  Argentina  ...   119000       None   4.0   \n",
              "48742  2019-08-14  58665  Argentina  ...   130000       None   5.0   \n",
              "49168  2019-08-14  59165  Argentina  ...   107000       None   5.0   \n",
              "49795  2019-08-14  59894  Argentina  ...   115000       None   4.0   \n",
              "49852  2019-08-14  59951  Argentina  ...   119000       None   4.0   \n",
              "\n",
              "       start_date surface_co  surface_to  \\\n",
              "200    2019-12-20        130         149   \n",
              "985    2019-04-15        123         123   \n",
              "1346   2019-01-31        160         200   \n",
              "1621   2019-03-08        157         168   \n",
              "2260   2019-05-09        161         168   \n",
              "...           ...        ...         ...   \n",
              "48649  2019-07-31        157         157   \n",
              "48742  2019-07-31        242         396   \n",
              "49168  2019-07-31        190         200   \n",
              "49795  2019-08-01        145         155   \n",
              "49852  2019-08-01        157         168   \n",
              "\n",
              "                                                   title          type  \\\n",
              "200    Espectacular 158m2 - UNICO 3 Suites - Dependen...  Departamento   \n",
              "985                                Departamento alquiler  Departamento   \n",
              "1346    Alquiler Triplex Los Molinos. Expensas incluidas  Departamento   \n",
              "1621     ALQUILER DÚPLEX EN PISO ALTO TORRE DE CATEGORIA  Departamento   \n",
              "2260               Venta piso de 4 ambientes con cochera  Departamento   \n",
              "...                                                  ...           ...   \n",
              "48649  ALQUILER TORRES BULNES GRAN RECEPCION 3 DORM. ...  Departamento   \n",
              "48742  Casa dentro de Edificio. Seguridad Coch Doble-...  Departamento   \n",
              "49168  Semipiso 5 ambientes con dependencia y 2 coche...  Departamento   \n",
              "49795  PALACIO BELLINI - PALERMO (APTO PROFESIONAL CO...  Departamento   \n",
              "49852  Departamento  duplex 4 ambientes en Alquiler -...  Departamento   \n",
              "\n",
              "          type_i18n                       geometry  \n",
              "200    Departamento  POINT (102290.361 107196.883)  \n",
              "985    Departamento  POINT (101574.557 108245.646)  \n",
              "1346   Departamento  POINT (109174.738 102231.768)  \n",
              "1621   Departamento  POINT (105163.595 105378.695)  \n",
              "2260   Departamento  POINT (104006.530 106075.998)  \n",
              "...             ...                            ...  \n",
              "48649  Departamento  POINT (105196.369 105467.289)  \n",
              "48742  Departamento   POINT (99932.663 107642.892)  \n",
              "49168  Departamento  POINT (103318.736 105729.364)  \n",
              "49795  Departamento  POINT (104547.555 105474.833)  \n",
              "49852  Departamento  POINT (105163.595 105378.695)  \n",
              "\n",
              "[164 rows x 29 columns]"
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              "      <td>Corredor Responsable: JAVIER HERNANDEZ - CUCIC...</td>\n",
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              "      <td>1662</td>\n",
              "      <td>Argentina</td>\n",
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              "      <td>58561</td>\n",
              "      <td>Argentina</td>\n",
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              "      <td>2019-07-31</td>\n",
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              "      <td>58665</td>\n",
              "      <td>Argentina</td>\n",
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              "      <td>59165</td>\n",
              "      <td>Argentina</td>\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-faf8d81f-8b69-4cc9-b776-977cf68ffe3e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-faf8d81f-8b69-4cc9-b776-977cf68ffe3e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-62eac63d-14d0-4478-8a8f-f49cf3eaf63a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-62eac63d-14d0-4478-8a8f-f49cf3eaf63a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-62eac63d-14d0-4478-8a8f-f49cf3eaf63a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VW58WJz1lfXP"
      },
      "source": [
        "Es posible combinar los filtros con otros metodos para describir o graficar los datos en una sola linea de codigo. Para eso filtro primero y grafico o describo despues.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fafijp3b09mc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e199884c-a914-4a9c-cc84-09ec9487b9a3"
      },
      "source": [
        "departamentos.loc[departamentos['l2']==\"Capital Federal\"].price.describe()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    3.347200e+04\n",
              "mean     1.855286e+04\n",
              "std      1.733385e+04\n",
              "min      5.000000e+01\n",
              "25%      1.150000e+04\n",
              "50%      1.500000e+04\n",
              "75%      2.200000e+04\n",
              "max      1.232134e+06\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BrBXPkuBKtLq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "5ba49176-3b25-4478-9795-4736971cfa24"
      },
      "source": [
        "departamentos.price.hist()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qiqWdr5VKb6q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "4442b416-650e-486c-80e0-9ce5337c2cf4"
      },
      "source": [
        "departamentos.price.loc[departamentos.price<30000].hist()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0v7Y1tQb8CEN"
      },
      "source": [
        "El método plot() en el GeoDataFrame permite mapear los datos. Podrán reconocer el AMBA\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-NqNb9oXjvkd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "543e4da4-014a-48f1-ac03-b45da606c98f"
      },
      "source": [
        "departamentos.plot()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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jvOZR7VgfTxe83CcEX5y6iUYLhqEaBtieXYyU+O6sp4uFVt+owfbsYtxUViPUzxOT48Lg6kyfRwlpiyhoEqsYqsxjiI+HC14fGoaZIyLgJJWgAcCX2TctuudNZTUA/eni+kYNPv+pyOaBa3VaAVKzivT2o2rXX+eLeKg4IUQYFDSJxcwVcm9OVdOAj45eRaSsE5JighHq52nxfbWP1R6zdqzwHu6q6vTarEgrxJ+HhRutW2sN7X2PXyrFnfLWHxaar79S4CSkbZEwDEOlU4yoqKiAt7c3VCoVvLy8xO6OXVFrGAxbe5zTfkxtZuuJuSOg1jCIWnSYc8UgqQS4tPxZvL0rFxkFpWbbj4wKwLY/DuZ2ExOmfXmG1X2BR32lqVpC7BuX93r6v5lYxJICBs2r9bg6SzEtPpzzfafFh7MOmABw7NJ9pHx+mvN9DN6bQ8AEmkac/866gUV7L2Dythws2nsBNfVqXvpCCBEHBU1ikaMFCosfqy1+MD85GjOGh4NN7o5UAswYHo5ZCZGcAhcAHLv0K1YeKrCkqzo19WrO9wWANUcuY/upW8i6eh/bT91Cz8XpmPblGav6QggRD61pEs7UGgbf5d2x+PHNix/MT47G7NFR2J5djOv3K1FaUQcJAE9XZ3i6SeEklSLcv4MusWfY2mMW3XPbiSLMSYxiPVXaMiP2+q+VFt3XkIyCUkz78gxSpwzk7TkJIbZBQZNwdrpICWVVg/mGLRir1uPqLEVKfHezj6+pV+P2A8tK9Gm3qkyOCzO7PcRQRizfMgpKUVOvNnhWKCHEflHQJEZps0SLy6oR5u+JBcnR8HB1sqi2rCXVelqO9rZlXed83+Y++d81rEwrNLk9ZHVagS7zVWh/O3ARq1/ubZN7EUL4QUGTGNQy6SXrKrD91C2Mig7En4aaHxW2JPN2x5Kx0ayr9Qgx2jM0Om6+PWT26CikZtkmYALAD5e5r5ESQsRFQZO0YipLNKOgFAzDmK0t69fBBRvH94Oypp5ztR5bjva0UrOK4O/pJuiUbEtqNWXSEuJoKGgSPWyyRI8W/oqNr/TFrK/zjBZNX/ViLOIjH+N8//pGjU1He1oaBvjkx2s2vefDukab3o8QYj3ackL0rEpjtzXj5I1fBSmavj272KajveZUNbYNYrUGbpd74wHC5h3SfeXeeGDTPhFCTKORJtFTXFbNqt3XZ+6g+H4VTswdwWvR9Kyrv1r8WEeU/NGPSJv1NAAgbN6hVtdf3noSAFC8ZoxN+0UIMYyCJtHDJdzlFJWjx4I03t7Q1RoGubfa18iqQFGJHvMPQW1mdB027xCur0qmc0IJERlNzxKdtPy7yLx6n/PjDI2QLHG6SImHte0vOcZcwNTqsSANB60oKkEIsR4FTQIASMsvwcyvzlv8+NQfb1jdB4WqhlU7ZwkQ4uVm9f0c0czdeXgt9Sexu0FIu0VBkyBdXoK3dp2zKgFn5eFCqK3M4FFW1bNq18gAH77SDwEdXay6n6M6cb0cjy/gZ3RPCOGGgmY7V9+owYLv5Lw81+kipVWP9+vIfvRY+rAWZxeORmznTgavO7fxtb9GDRC18LDY3SCk3aFEoHaqvlGDBXsu4MDPd1DHdlHNjImppzglBd1R1uDpdcdhyUYPbdH3p3oEQH7nYasiC68PDRNlv6ct1TZqsONkMSYOCaUEIUJshA6hNqGtHkK9Oq0AWzOLjFbzsUZgR2ecXphotp01yUMyLzf8NG8k1qUX2rxykL3aYsXeWELaOzqEmrRS36jBtqwbGLMpE58JFDABoLSyEf89+4vR68rKequzbZc+3wtqDdPmR5JcvLHjHNLlJWJ3g5A2j6Zn2wFbHHXV3Oz/5KODuzOSYoKh1jC64geL98mtqrrj4+mCNS/FIikmGNuybohWOchevbHjHO3lJERgFDRF1Dyg8FFNxxAxip8DTW/g/3g5FksOFVi193LmM48DYBDXPQBDevjrXp+bSnaVi9qbhf/JxerxA8TuBiFtFgVNkaTl38UHe+V4UP3ouCqZlzuWPs/++CxzrC1+7uvpgsc6ueHKvUqLHv/efy9YfG+t9xMjdedqHilQ6A6ODvXztPq526Kvzt3Dc/3u633AIITwhxKBTBAqEcjc6I9tUodawyDzcik+zLgCVU0DImUd8dEr/dHRvemz0LasG1h+qNDifkolEH0K9PWnwvD5yWK9NVipBPjTU+H490nbTTk7GhcnCR5/rCMGhPnqDg8nhBjG5b2egqYJQgTNtPymQgKm+Hq64OzCUUZHCmoNg4+PX8PGY1cMBo3enb2w///FY/E+Ob7MvslHt+2SuxNgTdW9Lm7A7Trz7f767ONYd9i2x4bxbVR0IFKnDBS7G4TYJcqetVNqDWM2YALAg+oGnLpRZvBaurwET67IwIajhgMmAOTfqcDT64+jq2/bnsI0FTBnDA/HYx1dDV7zdJXiyopnsX7KEFb36dclwJLu2ZWMglJM+/KM2N0gxOFR0LSh787cYt3270cut/pZurwEb+w4h/Jm66DG3Cyrwco0y6dmHd1nmUU4s3AUJg3p3OpabYMGH35/CYPC/RDs7W7yZJdg76YErbZwNFdGQSmGrPwev1awGF4TQgyi6VkT+Jie1WbIZhQo8O+fijk99tNX+yG5d4jueXouTkd9o8aifpDWZgwPR79uvnhzxzmj+1Z7h3TETWU1VLUaOEnYn0jiCGaPjMCMZx6HqzN9dibtm6DTs5mZmRg7dixCQkIgkUiwd+9e3bWGhgbMnTsXsbGx6NChA0JCQjBlyhTcvXtX7zmUSiUmTZoELy8v+Pj4ICUlBZWV+hma+fn5iI+Ph7u7O7p27Yp169a16su3336LqKgouLu7IzY2FmlpaXrXGYbB4sWLERwcDA8PDyQkJODq1atcf2WLpctLMGztcUxMPcU5YALAwn1yqDUM7ihrELEgjQImz1KzijAiKggJ0YFG2+TfrYSqtul1b0sBEwA+PHYVTyw8jNVpBWJ3hRCHwTloVlVVoU+fPvjkk09aXauursa5c+ewaNEinDt3Dnv27MHly5fx/PPP67WbNGkSLl68iIyMDBw8eBCZmZmYPn267npFRQVGjx6N0NBQ5ObmYv369Vi6dCm2bt2qa3Py5ElMnDgRKSkpOH/+PMaNG4dx48ZBLn9UfHzdunXYtGkTtmzZgpycHHTo0AGJiYmora3l+mtzli4vwZs7zqFEZfm9lFUNiFyYhqHrjoPCJf80DPB/P93AscJSsbsiqs8yiyhwEsKSVdOzEokE3333HcaNG2e0zZkzZzBo0CDcvHkT3bp1Q2FhIaKjo3HmzBkMGNC0CTs9PR3Jycm4ffs2QkJCsHnzZnzwwQdQKBRwdW1K5pg3bx727t2LS5cuAQBeeeUVVFVV4eDBg7p7DRkyBH379sWWLVvAMAxCQkIwe/ZsvP/++wAAlUqFoKAgfP7555gwYYLZ38/S6Vm1hsGwtcetCpjENgaE+uLszQdid8MuXFnxLE3VknbJrrJnVSoVJBIJfHx8AADZ2dnw8fHRBUwASEhIgFQqRU5Ojq7N8OHDdQETABITE3H58mU8ePBA1yYhIUHvXomJicjOzgYAFBUVQaFQ6LXx9vbG4MGDdW1aqqurQ0VFhd6XJU4XKSlgOow2NudqhafXHxe7C4TYPUGDZm1tLebOnYuJEyfqordCoUBgoP4akrOzM/z8/KBQKHRtgoKC9NpovzfXpvn15o8z1Kal1atXw9vbW/fVtWtXzr8z0HTWY3s3tlcAgr3dxe6GSVIJMDpaJnY37EaJqg5xq4/S2jkhJggWNBsaGjB+/HgwDIPNmzcLdRtezZ8/HyqVSvf1yy/GT+swRXvWY3v2u54hCLHzoDktPhx/HBoOqjb3SImqjpKDCDFBkKCpDZg3b95ERkaG3hyxTCZDaal+4kVjYyOUSiVkMpmuzb179/TaaL8316b59eaPM9SmJTc3N3h5eel9WYLN/r+2LsTXE//+4yCxu2GQVAJMiw/DsMcfw9z//Az/DoaLILRnlBxEiGG8B01twLx69SqOHj0Kf39/vetxcXEoLy9Hbm6u7mfHjx+HRqPB4MGDdW0yMzPR0PBoE39GRgYiIyPh6+ura3Ps2DG9587IyEBcXBwAIDw8HDKZTK9NRUUFcnJydG2E4iSVYMnYaABol4FTWxDA29MFof4eYndHJzzAE5OHdMOG8X2xK+cXTP73aXyXdxe/VtaL3TW79FlmEU3VEtIC56BZWVmJvLw85OXlAWhKuMnLy8OtW7fQ0NCA3//+9zh79ix27twJtVoNhUIBhUKB+vqmN6aePXsiKSkJ06ZNw+nTp/HTTz9h5syZmDBhAkJCmjbyv/rqq3B1dUVKSgouXryIr7/+Ghs3bsR7772n68e7776L9PR0fPjhh7h06RKWLl2Ks2fPYubMmQCaMntnzZqFFStWYP/+/bhw4QKmTJmCkJAQk9m+fEmKCcbm1/pD1mKK0tfTBc887gt3p7YZTiUAloyN1tXN/XHOCHTzs49p2qL71dh+6hbe/ToPVfVWFK1tR7b8aLt9zYQ4As5bTv73v//hmWeeafXzqVOnYunSpQgPDzf4uB9++AG/+93vADQVN5g5cyYOHDgAqVSKl19+GZs2bULHjh117fPz8/H222/jzJkzCAgIwP/7f/8Pc+fO1XvOb7/9FgsXLkRxcTEiIiKwbt06JCcn664zDIMlS5Zg69atKC8vx7Bhw/Dpp5/iiSeeYPW78lkRyNyZmYv2XsD2U+zL7NmjYG93LBlr+Giz/+bexuxvfxahV47jz0PD8O+TxXZ3cosUwP/efwbdAtp2LWPSftEpJzwR6mgwQyZvy0HW1fuC3kMokbJOWDq2l9lDtNPlJVh2oIC24xgwY3g4tmYW2fUGGGcpcG2V49fgJaQlu9qnSVpTaxhkXy/Dvrw7yL5eBrWGQZi/436KD+rkhjgWhx4nxQTjxNwR+GraEGx4pS/8OrgI0p8ngjrgicCO5huKTCoBPvl9H1xZ8Sz2/1xi1wETABo1wOMLDondDUJE5Sx2B9oL7TTtoQt38M2Z26hvVsg02Nsd8xOjOE3Pps2Mx6Rt2XhQ0yhEdzm5rKiAWsOYDZpAU5JUXA9/ZF8vg7LK/Gktlpg1MhINGg3e3Z0nyPPz5YPknhgWHYTnNmU6zOi7UQPcul9NU7Wk3aLpWRP4mp5Ny7+LhfvkJoOEBEBsFy/k3zZfhUgCoGjNGF19W3v4B1w0picCOrmZXLdtbl/eHUGD2l8SIrDhqH0nsUglsLv1SzY8XaQoWP6s2N0ghDdc3utppCmw1WkF+CyzyGw7BsCvD+uR0DMQR00UENcGTOBRhq49rBMuP/To7E5TCUFaQheA2JVzExLYd5E8RwyYAFDTQNtQSPtFa5oCSssvYRUwtUpUtUgZ1h2Ff0vC5CHd8GTXjrp9ni5S4Oisp3UBU0u7Trjzz4M59c3ZwEjQl6d9lQpVLd7ccQ7p8hKjbYQuAHHvYb1dB0xHRq8rac9opCkQtYbBwn1y8w1bKH1YCw9XJywfF8v6MU5SCaQS9uEn4jFPpP/ldzh1vQzZN+4DaFpnHNK9KZnn+Y+zWE0TG8OgaUS87EABRkXLDE7VagtAvLHjnMX34YvMyw1PRwbg+4uleFAtzDprWxM27xCK11AmLWl/KGgK5HSREsoq7pVmLJ225FIkPiygI5ykEgyNCMDQiIBW1/fPjEdlbSPe2nkW2dfLYMlsHIOmkfPpIiXievgbbJMUE4zo4I4oKKk0eN1WFBV1+PrMHVH74IiGrDiCUwsTxe4GITZFQVMglpx04uEixaBwP4vuxyXYbniln9k2Hd2d8WXKEN33lu6xNPU6qDUMrpVWcXo+Yj8UlY1QVTfA21OYrUOE2CMKmgKxZMS49sVYVts2DNGuEZoLar07e6Gju/l/9uaVjAI6uqGTmwv+mhgJZVU9/Dq6QVlZp5f8Y4yp1+F0kVJv6w1xPK//Xw72vD1M7G4QYjMUNAXCNohp9e7ihef7d7H4fto1QlNbUEL9PbD//8WbfS5zo8pgb3csGtMTwd7uUKhqDd5PAkD2W+F2Y+jcUcdX8ItK7C4QYlOUPSsQbRBjM25M6BmI/TPNBzNztFtQWh7+7OYkwUe/74Mf54ww+xzavZ+mgr1CVYu3d53H832atpS0/B213zcv3G6Io5w72jZL6/ODPvaQ9oaKG5jAR3EDQ6M2N2cpugd0wIAwXyxIjoaHqxNfXQbAvki8occNW3uc9ehY5uWGxc/1wvJD+r8fm32aze9nbLRqL7Qj6+WHCu2+r2LY8lp/s//WhNgzKtjOE74qAlkaxGwt+3oZJqae4vSYvyQ8gZkjHrf499OObAHD+/86uDqJdoyXu4sUyTEyxD8RCJmXOx5U1ePtXfZRgcmeBHZ0QfaCUXb5N00IG1QRyM5o663aO0vWGDccvYJIWUeLRxrGqhppR6sARNvLWdugwZ7zd7Hn/F1dn6YPD8fXZ2+jnPZz6pRWNpjcWkRIW0JBk+hYusZoqogBG0kxwRgVLTM6Wt3yWn8s3V8ARYW4K2gKVS22ZhbBy4P+t2npwl0KmqR9oEQgomNpaTttEQNraEfjL/Tt3OqYsaSYYCx+LhoeLuL+uTK/fans4GQZe7Pq0BWxu0CITdBHZqLTfNsK12LnQmwfUVbWY8LWk7j9oBbVDeKsa9qSn6cLlA487XtHWYPOftbXLibEntFIk+jRrjHKvLlN1fK9fWTgigz0X5GBK6VV7SJgjooOxIbxfcXuhlWe3fSj2F0gRHA00iStNF9jVKhqsPxQodE6umyKGHA1cEUGfq3kXrfXUf28eDS8PV1Q3+jYR25V1Lb9DzeEUNAkBjXP+PVwdTK4LYRtEQMulJX1dh8wO7o5obJOzct5nTOGh+tqt+befGB138RGtWhJW0fTs8QsY1O2Mm93bOZ5Y/uErSd5ey6hvDKgK7YYeD1cpICPhwv6d/XGvyYPwIZX+mLRmJ4YERmAlie3SSVNAXN+crTuZ22hrOBLn/wgdhcIERSNNAkr5raF8KX0oX2PMgEgIVqGuB7+rF+PlPjuqG/UYHt2MW4qqxHq54nJcWFwddb/zOooZQVNuV7muIlMhLBBQZOwJnSRhsraRtQ32u+6WMv1Wy6vh6uzFCnx3U220W75cfRSfTX1at5LQxJiL2h6ltiF5z/OQszSI6i25MRrG+Jz/bYl7ZYfR/fnL06L3QVCBENBk4ju+Y+zkH+7QuxumBQswPqtIdr1Yx8Px02m+em6EunyErG7QYggKGgSUVXWNtp9wPxLQgROzB1hs5M8kmKC8cmk/ja5l1De2HEOao0jTzITYhgFTSKqv3x9XuwumOTuJMG7CU/Y/ASPId394ezgp4aMX50mdhcI4R0FTSKqWw9qxO6CSRte7iPKfY/IS9Do4CO13Idi94AQ/lHQJKLq5mvftUpH9w2x+T3VGgYL98ltfl9CiHkUNIlRag2D7Otl2Jd3B9nXywRZo9rwSj9W7Z55gr8yfVz868Q1m9/zdJESyqq2sd9xyw/XsHifHNuybjh8mUBCANqn6dDUGkawYgPp8hKjB0PzmRDT0d0Zvbt4mUwG6t3FC//3pzjU1KvRc3E6b/dmY3XaFZy4UoatUwbabO9hW6gMpLXmyGXdf69MK8S0eP0qSIQ4GgnDMI69cCKgiooKeHt7Q6VSwcvLS+zu6BEyqKXLS/DmjnOtNthrw7EQWy+MbTvp3cUL+2fG676/fPchEjdl8npvtkZFByJ1ykDB75N9vQwTU08Jfh+xBHgCWfOSqAACsRtc3utpetYBaYNa84AJAApVLd7ccc6qPXJqDYNlBwoMVqTR/mzZgQLep2r3z4yHfGkiRvUMRKSsE0b1DIR8aaJewFRrGChrxCuzl1FQimlfnhH0HvWNGsjvqODk2ImzJt2vBnouThf8tSRECDQ962DMBTUJmoLaqGiZRVO1p4uUrYJxy3uUqGpxukjJe0m9ju7OSJ1qeCRnaGQthoyCUsHKxK1OK0BqVhEcPGmWNe2HEFuM3gnhC400HQyXoGYJtutptlx3MzayFssLH2ehpp7fGrmr0wrwWWb7CZhaGQWlSPj7D+i77HuM/sf/oLTzY+EIoaDpYIQOamxP2rDViRymRtZiuVJahZ6L03mrsVrfqEFqVhEvz+WIrt2vRnlNA66UVqH/igwMXJEhdpcIMYqCpoMROqhpT9owNrErQVPCkfakD6GZG1mL6Wjhr3j+4yyrnkOtYbD84MV2N8I05dfKegqcxG5R0HQwQge15idttLyH9nshT/poyd63X+TfrsC+vDsWPTZdXoJha49j+6lbPPfK8f1aWU9TtcQuUdB0MLYIatqTNmTe+qNVmY1O+mjOEQ5mnr/nAudsYntbp7VHr2w9KXYXCGmFsmcdkDaotcwmlfFYfCApJhijomWCFU9gyxEOZq6uV7PKJlZVN+BPn5/GnfIalFXV2+3vYy+ullYhXV5i0w9phJhDxQ1MsOfiBoCwFYHsiXZUBsBuA83GCX3xQt/ORq8/vf44bpbZd3F6eySBMMU0CGmOihu0E05SCeJ6+OOFvp0R18O/TQZM4NHI2tvTfg9mNjWNTAHTcgyazubke4sPIZaioEkcRnm1fRYxN5V4papuoIDJg56L07Fo7wWxu0EIBU1i/7R7Ne2RBKYTr/70OT97OQmw/dQt9Fx0WOxukHaOEoGI3bOHvZpRQR1QVFaDumbHW7UskK/WMDh1vQzZN+4DaJo6v1NOo0w+1TRoMGD59zi7aLTYXSHtFAVNYvfE2qvpLAXmJvXE1KfC4OosNZl4lS4vwbw9F/SmkD/+4VqbXWcW0/2qBoTNO4QDbw1DbDdvsbtD2hkKmsQi9Y0abM8uxk1lNUL9PDE5rimwCEGsvZqNGiCms7fu99ImXrWULi/BG79l97YkxMHdpMnYT08AAIrXjBG5J6Q9oaBJODN0GoeQBwyLuVfT3ChXrWGwdP9FG/WGGBI27xD+O/0pPNndV+yukHaAEoEIJ8ZO49AwwGeZRVidxn/CTvMqSLZmapSr1jD4/KciKCrqbNgjYsjLW08ibN4hsbtB2gEKmoQ1NqdxpGYVob5ZsgxftHs1gzq5Wv1cHd3YnYXp18HF6FYSbd3Y5YcKre4P4Q8FTiI0CpqEte3ZxWZP49AwTe2EkBQTjJPzE9D9MU+Ln+P3/Ttj3cu9WbVd8UKMwUQeqhtr3yhwEiFR0HQAag2D7Otl2Jd3B9nXy0RLLrmprOa1nSWcpBIcn/0MenexrKzhqpd6I7l3CGYMDzfZbsbwcCT3Dmn1c3s835O0FjbvEOZ9+5PY3SBtECUC2Sm1hkHmpVIs3i/H7XL9BJiW+wNtJdSP3QiPbTtr7J8Zj8raRoz5Zybrijspwx5l+M5PjkafLr6Yv+dnqGoflWjzcXf+LbAafm3tYc8oYWd3bjl25x6i7FrCKxpp2hHtiHL5gYt44oM0vP7lWfxS3jpjtERVizd3nEO6vMSm/ZscFwZz2w6lkqZ2ttDR3Rk/zhmBKyuexZDups8P7d3FC4ue66X3s7VHCvUCJgCU1zZibbrxdUpFBQVMR0PTtYRPFDTthDaxZGLqKWz7qRhqM/N/DICl+y/adKrW1VmKafGmpzWnxYcLtl/TGFdnKXZPj8OM4eGtgroEwJ+HhmP/zHi9n5sqon5TWYOBKzIMXlNWUqasI6KpWsIXCpp2wNLEEkVFHT4+fk2gXhk2PznaYHCSSprWAYXYp8nW/ORoXFr+LBaN6YkpcaFYNKYnLq94FgtbbFdhU0T918p6/Of0zVY/9+tgffZu7y5e2PJafwR72/8B223F7txysbtA2gha0xSZtYklG45eQaSso03XN+cnR2P26CibVQTicm6oq7MUKfHdTT4f2yLq7++Rw9PdVW99U+btwb7jBvTu4qUb9Y6KluH9b/LwXd5dq56TEGI7FDRFxkdiybIDBRgVLbNpnVM2wYkP6fISLDtQoPcaWZII1TzwFt2vYv24t3adwxbpo0OQtdWJLPk3W/dyb4wf2FX3vZNUgpf6d6GgSYgDoelZkfFRjLxEVYsNGVdE3Y4iBGPT1gqOiVBp+XcxcGUGJqaewru786DkeC7nsgMFutdVW53Iko8nQV5uFjyK8GXM3+lYMWI9Cpoi46sY+cc/XMPE1FMYtvY4L1m19Y0abMu6gcX75NiWdUOQKj+mmJq21v6seTAzZnVaAd7adR7KKssPsC5R1eJ0kVL3vbY6ka+nC6fnmfp/ZzDtyzN6P8tp9rxEWBfva1BTrzbfkBATKGiKbFC4Hzxc+Ptn4DoKM2R1WgGiFh3G8kOF+DL7JpYfKkTUosOC1JU1RFvT1dQUKIPWwayltPwSfJZpuuwfWy1nBJJigrF4bC8jrY3LKCjVC5wnr/1qdd8Ie+99TYeCE+vQmqbInKQSPNbJDbeU/BxWzKBpm4Wl65zaguwtaQuyA+A1Q7blEWNBndyxIq2AdRF0Y9Pbag2DhfvkvPXT0IyAzMuyWYKMglLU1Kvx0dHLOPeLytquEQ4OX6SRPbEOBU070NXXk7egCeiPwgyd/2gM24Lss0dH8ZIpa+iIMa6MTW+fLlJCWVVv+RP/RgJA5u2uK9zePMh39fWAzMsd9yq4H1m2/OBF7D7zi9X9I4TYFgVNOzB9WHf8dL2M9+flmmTEpSC7tZmzxka0bLUMZi3xkWClHaMvGhON00VK/CvrOo5f/hUMo9/Gkph/tLDUqg8LhBBxcB4uZGZmYuzYsQgJCYFEIsHevXv1rjMMg8WLFyM4OBgeHh5ISEjA1atX9doolUpMmjQJXl5e8PHxQUpKCiorK/Xa5OfnIz4+Hu7u7ujatSvWrVvXqi/ffvstoqKi4O7ujtjYWKSlpXHuiz0YFvkYXATYLcI1yUjoguzaMoH/zb2NrVYGTABYMjba6PQzHwlWMm93TB8ejuWHCjAx9RSOXdIPmIBlARMASh9SZSExvD86QuwuEAfHOWhWVVWhT58++OSTTwxeX7duHTZt2oQtW7YgJycHHTp0QGJiImprH33ynzRpEi5evIiMjAwcPHgQmZmZmD59uu56RUUFRo8ejdDQUOTm5mL9+vVYunQptm7dqmtz8uRJTJw4ESkpKTh//jzGjRuHcePGQS6Xc+qLPXCSSvDPSf15ez4JmvYyGhuFGcNXQXZDmbfNywTO/vZnq04JkXm7Y/Nr/U3u0xwU7sf63ExjHuvoiq2ZphOSiGN5MpT9cgUhhkgYpuVnZw4Plkjw3XffYdy4cQCaRnYhISGYPXs23n//fQCASqVCUFAQPv/8c0yYMAGFhYWIjo7GmTNnMGDAAABAeno6kpOTcfv2bYSEhGDz5s344IMPoFAo4OraVLZs3rx52Lt3Ly5dugQAeOWVV1BVVYWDBw/q+jNkyBD07dsXW7ZsYdUXcyoqKuDt7Q2VSgUvL8uOouIiXV6Ct3acA5fNHS2nB7XjLnNBxZD6Rg2iFh02OW0olQCXlj9rdE1zdVoBtmYWCXZ01ri+IfjDk11xv6oOgZ3c8WSoL3JvPsDdB9XIu10OQIIw/6YKRZ/+cA0fHbO/mQUinuurkm1aBIQ4Bi7v9byuaRYVFUGhUCAhIUH3M29vbwwePBjZ2dmYMGECsrOz4ePjowuYAJCQkACpVIqcnBy8+OKLyM7OxvDhw3UBEwASExOxdu1aPHjwAL6+vsjOzsZ7772nd//ExETddDGbvtibpJhgXF2VjMxLpfjw6BXcVdWgvLqhVRDz9nDG2t8OUm5ZLUdmxbFh2oLsptYaXx9quFyeWsPg3d3ncTBf2JNXDl0owd5mFXSkEhgM8ivTCpEyLBxuzlLU2XiPKbFfysp6PEZFJogVeA2aCoUCABAUFKT386CgIN01hUKBwMBA/U44O8PPz0+vTXh4eKvn0F7z9fWFQqEwex9zfWmprq4OdXWP1poqKirM/Mb8c5JK8Ex0EJ6Jbuq3WsPg1PUyZN+4D0CCuB7+GNLdX/dpeVS0jHVdVja020mMBc5tJ4rhLJXobTtJl5dgyb6LuGeDdbqGFse/GBsVa5imTN+EnoE4WlgqeL+IY3jh4yycXJBgviEhRlD2bDOrV6/GsmXLxO6GHiepBEMjAjA0IsDodS7bSthIv2j4Q4VW8/2a2lJ39poIevxSKVKGhWLbidYnlpD2xxYf7EjbxmtFIJlMBgC4d++e3s/v3bunuyaTyVBaqv/Jv7GxEUqlUq+Noedofg9jbZpfN9eXlubPnw+VSqX7+uWX9rePjs2xWQCwNbMINfVqzNtzwW4DJtA04gzx9sSnr/aHEy1lEUKsxGvQDA8Ph0wmw7Fjx3Q/q6ioQE5ODuLi4gAAcXFxKC8vR25urq7N8ePHodFoMHjwYF2bzMxMNDQ8qheakZGByMhI+Pr66to0v4+2jfY+bPrSkpubG7y8vPS+hKbdhrEv7w5+unofP127j315d0Qrvs722CwGwIztZ1HOsfi5GIruV2F7tvmDvUnb182XzjAl1uE8PVtZWYlr1x4dfFxUVIS8vDz4+fmhW7dumDVrFlasWIGIiAiEh4dj0aJFCAkJ0WXY9uzZE0lJSZg2bRq2bNmChoYGzJw5ExMmTEBISAgA4NVXX8WyZcuQkpKCuXPnQi6XY+PGjdiwYYPuvu+++y6efvppfPjhhxgzZgx2796Ns2fP6ralSCQSs30Rm6Fjr5qz5Agsa93lsL3i1A3+CzIIYUfOLbG7QOzE4B6GlzkIYYtz0Dx79iyeeeYZ3ffaDNapU6fi888/x1//+ldUVVVh+vTpKC8vx7Bhw5Ceng5390ef8Hbu3ImZM2di5MiRkEqlePnll7Fp0ybddW9vb3z//fd4++238eSTTyIgIACLFy/W28v51FNPYdeuXVi4cCEWLFiAiIgI7N27FzExMbo2bPoiFjZrgSW/FV+3ZPuIpUI4nBVZT0M34mAGhnLbu0xIS1bt02zrhNqnqdYwGLb2OOvgFOztjhNzR9hkf5mqugF9/va94PchRAxfTRvCe+IccXxc3uvpaDARnC5ScqoyY+4ILD55e7og1N/DbLvnettuytgSlPNDWpJ5uXGukkVISxQ0RWBJMXE+CpCz9eOcESYD54zh4dg4oR+Cvd1NBiexslXdnSV2ndFLxOHj4YI3dpxFaqbtD1UnbQcFTRFYUkycjwLkXPw4ZwR+Xjwa/bt6oZObEwI6uGLOqAhcWfEs5ic3FUpfMrapwIGx2DgqOsjIFWHVNlLIJK1duleJjIJSrEwrRKQND1UnbQsVNxDBoHA/BHNIuPHv4CrKtJK3pwv2vB1v9HpSTDA2v9a/VQawNut3T+5tW3STEM4YgQ5VJ20fJQKZIGTB9nR5Cd7YcY5V209f7Y9kO15DVGuYVqX8Zmw/g6OFv4rdNUJMMncAAWkfKBHIASTFBGPLa/3h4+list2M4eF2HTCBR6X8XujbGXE9/PHGjrMUMInOtPhw7PzzYMx85nH07eotdnf0aA9VJ4Qtmp4VUVJMMEZFy/Dx8Wv4v5+KUF7zqLqOXwcXrHghBsm9Q0TsIXc19WpkFFCBdAL4erhg5Yuxug99Qx8PwPuJkVidVoDUrCKTR9DZkqWHqpP2iYKmyJykErybEIGZIx7n9bQSsayi5Ip2L6FnIFKGdTf6Nzw/ORqzEiLRc3G6CL1rje3h64QAFDTthhCnlYihuIw+tbd3ClWt2Q99J6/az/R9UCc6X5OwR2uahFdh/vSpvb2T363AsLXHkS43fiD58rRCG/bItP+3O89kXwlpjoIm4dUCG6bv+3q64Pf9u9jsfoQ9bd1kY8HofqV9nWu57ECBKKcKEcdD07OEVx6uThgVHWh1MpAEQHBHJ0SG+OJBdR3uqurQ0KiBzMcdT0cEIv6JxzCkuz/UGgb/OUf7Qe0Rg6ZgNCpapjdVq9YwqKpTi9exFhg8KlXZFpZIiLAoaBLepU4ZiGlfnrEqcDIA7laqUXmrHPlLE422c8RkqfbEUDAa/9lJuyxzaMtSlcRx0fQsEUTqlIEo/FsShkcEwLVFEVofDxfMfKYHXhvcDfERAXAx8VdYUduI3kuPmLyXp6knIKJTqGp0/11Tr0buzXLxOmOCrUtVEsdEI00iGA9XJ3yZMthgxSDtCPHXijoMXHXU5PNU1Dbi14o6POZlOMsx/d2nMfzvP/Def8IPZVW97r/tdUtSRzepwVKVzf92Azq6AQxwv6rOobeFEetQ0CSCM7Wd5sVPT7B6jhc/PYET80YaDMDdAihj1575errq/vvQBfvMUlVrgIP5d9HJzQW7corxS3kt3JwkuPewDvcqDCctdXJ3xupxsXiur2MVICHWoaBJRKWsajDf6Ld26fISg8XhP3g2CkN7+OGn67Y5c5Rw8+tvmbLTvjzD+t/b1moaNHh3dx6nxzysbcTM3eexL/8OUqcMFKZjxO7QYhARlV8H07V3tdxdpHhzx7lWJ8OUqGoxc3ceBUw7VnC3ok2XV8woKMUrW07SGZ3tBAVNIqrv3hrGrqGm0S4zLol5NQ2NdruWyZec4geIXEhndLYHFDSJqB7zcoOXu+lVAk9XKZS1FDId1cAw/3ZRXpFB0xmdFDjbNgqaRHT5SxONBk4vd2esfqm3jXtE+CIBMPWpMNHKK0bLOtn8nqlZRTRV24ZR0CR2IX9pIs4sSEAXH3d4ujihi487zixIQP7SRF72z9HOAHFMHx4OV2epTcsrakUHe+G/bw21+X01DLBgzwWb35fYBmXPErvxmJcbTswb2erng8L9EOztDoWqlvO65ri+IXhlYDc8qKrH27vOAQCtjdrIjOHhmP9bsOSrvCIXL/fvDA9XJ8R09oL8ToXN7gsA/zl3G8cv3cORWU8b3V9MHBONNIndc5JKsGRs05svlwGjVAKs+30fxPXwR3LvYGx+rT9k3rav+hIfEYCdfx4Mj3ZSuWhsHxmurHhWFzC1UqcMxKjoQJv1Y3JcGADgAxFGuQCgrG7AwFVHzVa0Io6lffxfTBxeUgz3oDctvmlqsPlznJg7Al9NG4J//KEPpwBsjd898RiGPh6AN55+3EZ3FM+nr/bHPyc+qfe6N6ctrzh5SDfEdvYSrB8BHV10fdDOVIilorYRUQsPI/t6GZ2k0gZIGIahf0UjKioq4O3tDZVKBS8v4f4HJ+y1rAh0/NI9bDtRhObvRVJJU8BsOdJpaXVaAT7LLDJ6/YmgDrhyr8qq/kolwKXlz8LVWQq1hsGTKzJQXm2fG/yt4SwBPp7UH0kxwawfo9YwGLjyqF6ZPb68NrgrVrz4KIEsXV6CN3ac4/0+XEklgLNUisBOrvjurWE0dWsnuLzXU9A0gYKmY6hv1GB7djFuKqsR6ueJyXFhRkc6La1OK0BqluGgW9OgwZfZN63qW/N1PcD4m7cE1q+1SgB4e7qIEpQ/SO6JacO7c37ch+mX8M//Xee9P5vG98HzLc5aTZeX4N3d51HXaD9veZ3cnHBhWZLY3Wj3KGjyhIJm+2As6G7LuoHlhwrNPj4u3B85xWWsR7uGygF6ujqhpkENY/83SiRAQs9AnCl6gPKa1kFRO9U8K+EJbDh6xWyf+dR8NM3VrN3nsTfvrgC9Ara81nrkuyatEFsybwhyP0u5OQGXV44RuxvtGpf3esqeJe2eq7MUKfGtR0mT48KwMq0QppahpBLgi5RBAMB6tJsUE4xR0TLdNHNGwT0czDdeyHxIuC++TBmim+L9+Pg1/N9PRXrBU+btjiVjozEqWobdZ25ZlGlsqZZrx1xU1wt3GPX8PRdaHYD9sM7+psbr1MDUbad1f0fEvlHQJMQIV2cppsWHm1z3bB4wDAVeY7Qnv9Q3avCXr/NMtj1d/EDvce8mRGDmiMeNHre2ZGw03rTB+p0ETfswza0dmzIwzA/fF9zjr1PNPKhuwKkbZRj6eIDuZxKJfW7Y/fHqr1h5qAAfjBEn05ewR9mzhJgwPzkaM4aHtyqOIJW0Xq+0xPbsYpMjWaBps/zc//ysl32pDbov9O2MuB7+eqMpbaaxp6uTVX0DgN5dvAwWhojr7ovLBraVsKXWMMi+XoZfHliXaGXOlyf1P/CE+3cQ9H7W+NcJqiTkCGhN0wRa0yRa1iQbmbJ4n5xTslHwb9OwbLJUZ3+dh/+ev8PqeVsmIjVfk+X7dze0piuk5mub9Y0aRC06bPaDilgiHuuAv42LpQOubYwSgXhCQZMIjW2ykZb2bXSzgSSX5rhusZAvTcTXZ27x/qHAUL/e3HHOplWZgr3dcWLuCF0QMrfVyB5w+XBErMflvZ6mZwkR0eS4ME51cbXBZtmBAqMb5dUahtOByp6uTvBwdUJKfHf87YUYpMR3FyRg1jdqsOA7uc3LGJaoavH5T0W618vUlHv/rt427p1hClUt3txxDuly4wliRBwUNAkR0YffX+I8VcigKRCcLjJ88PY7X51DHYe1sep6tdHn4ku6vASDVn4vSCEDNpYfKsSwtcd1QWh+cjQuLX8Wi8b0xJS4UCwa0xOXlj+LZ2NDROlfS2w+HBFxUPYsISKxdpqw9GHrNcH6Rg0OXVDw8lx8sZdqPNrRm3Zq29BWo+L7lSL1rrXmH47ieviL3R3yGxppEiKC+kYNUrOsW1czdGTa9uxi3p6LD2oNg/e++VmQ5+aKzeit9GGd7TrEkpAfaAh3FDQJEQGbrSbGSNCUKDIo3K/VtZvKas7P5+niZPC5+HDy6n1BCxhwZW5qO9BLvMLuxgj1gYZYhoImISKwJLhpMWgqYGBoS0Konyfn50uODRZse8N/z98W5HmtZWz01j3A/vZxHr8kTPEHYhkKmoSIwJLgxgbXbFwJgFUvxQrSF0DYMnnWMDZ64/r62UJqVhFWpxWI3Q3yGwqahIjA2jdnY+ty2tJ/bP3ZirqxbAwME2ba11KmprYB7q+fraRmUbUge0FBkxARWPvmbGpdTrsP0VxMHhUdKHit06lPhcFeyr1qu2FsalvL2D5OAKKNQjUMMOLvP2D0hh8x7YszqKxtFKcjhCoCmUIVgYjQJm49iewbD8w3NGDjhL54oW9no9frGzWYvycfB/Pv6p0h6eXujFXjYvFcX9vsSRSrAo9fB1e9faFcq+y0LB/4RXYxbilrOPVh8pBuOFqgQEkF//tTe3fxwv6Z8bw/b3tER4MR4iAigrwsDpo+bi4mr7s6S/Hh+L5Y9/s+Rk9EaYskaDoq7cc5zyD35gOLf+/m+zi/y73NOWACwKLneiE5NgQTU09xfqw5+bcr8PzHWRQ4bYyCJiEisiYhKOPSPTzdM9BsO+2JKGLgYz+qJZaMjYars9Tq31utYXDqehlm/8fyvaaDwv0Q7O0uSIH6/NsVqKxtREd3eiu3FVrTJEREk+PCzK49GlNcJuyxWnywZj+qJTxdncwWs2crXV6CYWuPY9K2HIt/h+3ZxXCSSrBkbLTF/87m/OXr8wI9MzGEgiYRRH2jBtuybmDxPjm2Zd2gzD8jXJ2lGNPbsjd4Dxfrz8sUmjX7US3B1xYX7Wks1o4O/3uu6Wg27Rmnfh1MT6lb4tYD7tPGxHI0pidWqalXY1VaAYrLqhHm74kFydH46OhlpGYV6X06X5lWqDufkejbOKEfjl8q5fyGn9hLJlCP+HNV8dDm91x2oACjomUWr9uqNQyWHSjg5TSWwpIK1Ddq4OosRVJMMEZEBWHI6mO8Fq7v5uvB23MR82ikSSw27csz6Lk4HdtP3ULW1fvYfuoWei5Ox2eZRa2mszQM8FkmbdI2xEkqwT/G9+H8uM6+whRI4Et9owY5xcKenmKIqe04bJwuUvK2/shAvx6wq7MUq16MgQTgbbq2i53/HbQ1FDSJRaZ9eQYZBaWcH0ebtA1LignGltf6I9ibXZ1RUxv0xWBoOt7W65nNWVPknO8C6S2nqLVTtTKW/9bmfJFdTP9P2RBNzxLOaurVFgVMoGnEuT27uNWRTKTpzXRUtAyni5TIKFDg3z8VQwLoTROy3aBvS6vTCgxOx0fJOonWJx93bmuHag2j25ZTWsFv0DSUId3837r0YS38PF0x5d+nLZoSpv+nbIuCJuFslZVTrLZODnEk2u0hcT38MSjcD8sOFOhNFco4btAXmrHCBRoGKCix/XqmVkahAk9Hmd+OAzQl/bR8nfkilTRlSBvScivQ9OHhFheBKC6rRvb1snazF1dMFDQJJ2oNg8MXSqx6DqGKlbc1LUcj9vZmKNYeTDaOXfoV4Vk3MDkuzGRtXW2WrFCzyNM41PbVJslZEjj35t3G9lM3dd9zrX5E2KMyeiZQGT196fISzP7mZ1RZkdYvlQCXlj8raJFwYhvbsm5g+aFCsbthklQCo1nbag2DYWuPCzbCtDRbvL5Rg+jFh2HNMqX2YxVfe1bbOiqjR3iXLi/BGzvOWf08XD55txWnrykx/l/Zuu+/+XMcBj1uP0k8lmI7zd4rpBMKSx6KkhSkzdq+U16LUdFBeqN1PrNktXqFdMJL/bqYHeGa4uosxbVVY6Aor8Vz/8yEsqoBXOMng6bAae32G9IaBU1iVn2jBu9/m2/Vc1jzyduRhc071Opn2gBavGaMrbvDK7bT7C/164LJb4Vh+cGL2H7qlsC9MuxgfgkO5jctK2inLuuszDjVJmk9/cRjGB4RYFWgNETm446zi0YDMJxsJZXA5AcRBo+234hVRrEtoulZE2h6tmmEOX/PBTyobrDo8aF+npgSF8r7G4ojMBQwW3LkwFnfqEHUosMm37ibT8erNQz6L8+AqsayvyW+aMdcsxIisOHoVYufR+blhqXP97LZ9Kd2G09xWTUABmqGwa6cX8w+ztxpOITbe337ehcjnGiTJCwNmABw60F1uwyYp6+x21zPtp09YnMmaPPpeCepBH8aKv4Bz9oY/9XpW5B5Wb5X8sPxfW26XujqLEVnXw8cLbyH7adusQqYABDQ0U3gnrUv7eudjLDGVykxhtGviNJeNF/D5KOdvZo9OgpDurden5VKgBnDW0/HzxzxOHw8+a+/yhUDQFFRhwFhvhY/x/3KOv46xILF9XBpLpFXtKZJDOIzSYL2ZbZNhtbZJACGhPvhi5TBBmcXnKQSrBoXi7d2WZ9UxgftOqclAjvxU9GHDWs+xN6vsm1wb+soaBKD+CwlRvsy256VhwoM7tFkAGQXKTF41VEMCPXFhlf66Z31mC4vwTu7HfsoK+0h17YsY2jNh9griofYlnUDHd2ccLSwFDUNGoQHNB2u4OFq/yfl2BsKmsQgPj9FG6uI0pZ98+c4VlOv3/w5zga94dfBvLtmixo8qG5ARmEpYpYeQXiAJ46+9ztkFCh42bYkJrHKGFrzIfaT/11v9bMT14Dtp25hVHQgUqcMtKZr7Q4FTWIQX6fNvz40tN0lAQFgvQ/T0fZrpstLMJPjSLHofjUiPkhDR1fb/h24O0tQ28i0qt/LRcvHGipjaOh4PL5HcEJNBWcUlGLal2cocHJAW05MaO9bTqwtaBDq74Ef54zgsUeOx9S2E0fbbqLWMIhdmo7qesc4UePTV/tBKpVYVVd2++uD4OwsNVrG0NhpP7GdvbAgOZq3sofa6kUKVa0geT2Ff0tq11O1tOWE8EJ7XJWnBf8zjYx8rN0GzObHZC0a0xO7/jhY7/o3f45zuIAJAL/ffMJhAiYA+HZwQ1JMME7MHYGdKYPh48E+a1eCpiIIT0UEIK6HP17o2xlxPfxZBUwAuHCnAhNTT2HY2uNIl1tXqxloSqBaMjZa17eWfZUAeHdkhMXPb+0hDO0J70FTrVZj0aJFCA8Ph4eHB3r06IHly5ej+YCWYRgsXrwYwcHB8PDwQEJCAq5e1d9krFQqMWnSJHh5ecHHxwcpKSmorKzUa5Ofn4/4+Hi4u7uja9euWLduXav+fPvtt4iKioK7uztiY2ORlpbG96/cpiXFBOPC0kQkxwSxfsym8X2x7fVBAvbKfq1OK0DUosNYfqgQX2bfxPJDhXjtixzMGB6O4jVjULxmjMNNyQJNAeL8LxVid4MT7Tqgk1QCqVSCcpZFFdisW7I9Hq9EVYs3d5zjJXAaO4dT5u2Oza/1R2pm67VLtpoKJhA2eA+aa9euxebNm/Hxxx+jsLAQa9euxbp16/DPf/5T12bdunXYtGkTtmzZgpycHHTo0AGJiYmorX00hTJp0iRcvHgRGRkZOHjwIDIzMzF9+nTd9YqKCowePRqhoaHIzc3F+vXrsXTpUmzdulXX5uTJk5g4cSJSUlJw/vx5jBs3DuPGjYNcLuf7126TaurVWLT3Av74f6fh39ENU+NCTbb3dHXCltf64/n+7bP6iPaYrJYVcrT1T1c76Kd5a85PFVPzdUAuiTTaIGSqcMHfDlzk1JdlBwqg5qH4rnbk/NW0Idg4oS++mjYEJ+aOwMPaRlQ3WD4LEOZPGe5s8b6m+dxzzyEoKAjbtm3T/ezll1+Gh4cHduzYAYZhEBISgtmzZ+P9998HAKhUKgQFBeHzzz/HhAkTUFhYiOjoaJw5cwYDBgwAAKSnpyM5ORm3b99GSEgINm/ejA8++AAKhQKurq4AgHnz5mHv3r24dOkSAOCVV15BVVUVDh48qOvLkCFD0LdvX2zZssXs79Ke1zSNTT2F+nvgVllNq3WV53oHY+OEfu22MDTXknL2qPlBzNr1OwB44eMsyO+KdzYmV9otISfmjtD9PWZfL8PE1FNmH7toTE/8cWi42b/jIauOQlHBbf/jV9OGCFIDVq1hELnwMBqtCMq0piniKSdPPfUUtm7diitXruCJJ57Azz//jBMnTuAf//gHAKCoqAgKhQIJCQm6x3h7e2Pw4MHIzs7GhAkTkJ2dDR8fH13ABICEhARIpVLk5OTgxRdfRHZ2NoYPH64LmACQmJiItWvX4sGDB/D19UV2djbee+89vf4lJiZi7969BvteV1eHurpH/yNUVDjWdBRfTK3V3Cyrwciox/BUjwDcVFYj1M+zXZbJa2l7drHZUzw0v1VHSonvbptOcWDoIGYfTxeAAetpTXvBoPXUqjYb3FgijTbQsgmYluJz73NzHx+/alXATOgZ2K4DJle8B8158+ahoqICUVFRcHJyglqtxsqVKzFp0iQAgEKhAAAEBemvkQUFBemuKRQKBAbqn7ru7OwMPz8/vTbh4eGtnkN7zdfXFwqFwuR9Wlq9ejWWLVtmya/dZrCZijt26Vd8/OqT9D9aM2yrHtljdSRjBzGXW1FzWGznbz3Qm17VJtK8ueNcq20kluy9/F3kY9h95janPvG9baS+UYMFe/Lxn3N3LH6O3l288K+ptN2EC96HB9988w127tyJXbt24dy5c/jiiy/w97//HV988QXft+Ld/PnzoVKpdF+//MKuIHJbwjaLjrLt9LGtemRv1ZH4qjFsb1KzilDf4ugvc4k0XIqvLxzTi3VbbSYunxWEVqcVIHLhYasC5qbxfbB/ZjxvfWoveB9pzpkzB/PmzcOECRMAALGxsbh58yZWr16NqVOnQiaTAQDu3buH4OBHf6T37t1D3759AQAymQylpfqjncbGRiiVSt3jZTIZ7t27p9dG+725NtrrLbm5ucHNrX2fCMA2i46y7fRNjgvDyrRCs2ua9lYdSYiDmO2BsanwpJhgjIqWtVq75Tole+GOilN7PisIaRPOrBUd4mN9Z9oh3kea1dXVkEr1n9bJyQkaTdOnvvDwcMhkMhw7dkx3vaKiAjk5OYiLayopFhcXh/LycuTm5uraHD9+HBqNBoMHD9a1yczMREPDoymkjIwMREZGwtfXV9em+X20bbT3Ia2xzaKjbDt9XI/JshdCrbPZA2NT4U5SidG9l2yxfd18PFw4j2JNqW/UmC1hyNazm37k5XnaG97/Dx47dixWrlyJQ4cOobi4GN999x3+8Y9/4MUXXwQASCQSzJo1CytWrMD+/ftx4cIFTJkyBSEhIRg3bhwAoGfPnkhKSsK0adNw+vRp/PTTT5g5cyYmTJiAkJAQAMCrr74KV1dXpKSk4OLFi/j666+xceNGvcSfd999F+np6fjwww9x6dIlLF26FGfPnsXMmTP5/rXbjAUtjnKytl17Mj85GjOGh6Ple7CxY7LsgS1P6rC1rr4egj0320IJ/+D5zE02CWdsWbFDpV3jfXr2n//8JxYtWoS33noLpaWlCAkJwYwZM7B48WJdm7/+9a+oqqrC9OnTUV5ejmHDhiE9PR3u7o/+B965cydmzpyJkSNHQiqV4uWXX8amTZt01729vfH999/j7bffxpNPPomAgAAsXrxYby/nU089hV27dmHhwoVYsGABIiIisHfvXsTExPD9a7cZHq5OGBUdaDIZaFQ0ZdsZMz85GrNHR2F7drFDZBebyyp1ZFEy4baJfV9gOJmwpWOXFBjRM9B8Q5b4TCRzsc8/SbtHtWdNoH2arQMnnYrQ9mizZwHxziu2pqi6MRsn9MULfYUptDF5Ww6yrt432y4+IgDbUwabbcfWtqwbWH6okJfnOjrraTwu68jLczk6UfdpkrYhdcpAm5zeQMSnzSptuU+zg6sTqurVgtzz7Wd6wM/TFX4dXCHz9kDOjTJ8dOyq+QdyIOTUc5i/J7JYdJfvtX82CWdsSAAKmBaioEmM8nB1wvJxsWJ3g9iAoaxSjYbBpG05gtzviaBOeqNAvhOS+N7i0dKC5GhsP3WLVTs+uTpLMbKn6eUTcyQAihzwwAB7QUGTEALgUVapllrD8HKmqiEtR4F8jwonDOwmaElHsdb+1RoG8jvcKpU5SQA107SGefgdmpK1FgVNQtoJQ7VlTQUWbRUda85UNcTQKJDvhKSwAOG3RKVOGWjztX+u+2qDW9TgJdajoElIO2CotqyvpwsmDuqK/F9UUNU2oE8Xb3wwppfe6MiS7RLuzlLUNhrezyCB4Y3+psrcWcJWW2lsvfbPZRrb2GtNrEPZsya05+xZ0nYYqy1rTMtRUl5xOcZt+cns48ZE++C1oVEYFO6HI/ISLNwnh7LqUfGRYG93LBkbbTIQp8tLsOA7OZRV9Sx725qPhwtyF41qk8GC7Wkt/h1csfLFGF73iHLBdVZDbJQ9SwgBYFlt2YyCUgxbcwwZ7/0OHq5O6Bvmw+pxn0wZqvvv5N4hSIwJ5vzGmRQTjJp6Nf7yzc8ceqzv9aFhdv0GbQ0209h+HVyQPX+kaHuDDc1q+HVwxYoXYpDcW5wgzifa3kpIG2Zpbdnb5bXouTgdA5Z/j8raRhSbybY0dN3ScnUyb8sr+fh6umDmiAiLH2/vtNPYwKPTWbQkv32tejFW1ID55o5zrf7mlFX1eGvXOYc9iL05CpqEtGHfXyyx6vH3qxoQs/QInv84C8VrxmDvG0P1ru99Y6jZgMqVdjTFdawoAbD6pdg2O8rU4vO0Fj6xmdX4LLMIafl3bdYnIdCapgm0pkkcmVrDoO/fjuBhLT8FCnp38bLZUVLp8hKDWbvaJCFPVydUNyu8wGa9tK2xt3VDtuutfh1ccOYD+1pzpjVNQghOFyl5C5gAkH+7ApW1jejobpu3DR9Pl1YHYWs/4WsDpo+HC14fGoaZIyLs6k3YFlruqxUb28xeZVUDThcp7arvXND0LCFtlKKC/6IEf/n6PO/P2ZJ2XaxlwDREVdOAj45eRQbLAupEOFy2+WgDbH2jBtuybmDxPjm2Zd1odXC4PaKRJiFtULq8BAv3XuD9eW89qOH9OZvjmu3LoGnKdtmBAoyKlrW70aY9GRTuB78Orqy2CwV2csfqtAKkZhXp1dFdmVaIafH2eYyeFo00CWljtOuBVXX8F1vvJuAZlYBl2b4MgBJVLU4XKYXpFGHFSSrBihfMH7vo18EFWzOv47PMolaF5zVMU7KQPWfZUtAkpA1RVtbrjvkSwoZX+gn23IB1hdv5LvpOuEvuHYwZw8NNtlFWNeCHy7+abJOaVWS3U7U0PUuIHbIkM/Lp9cdxs0y46VM3Z6ngSUDWlL+zVek80lrzv9ffRQYhNsQbiw9c1KsIxYWGAbZnFyMlvjvPPbUeBU1C7IyhiirmtlQIHTABYHhEgKDPD1heuF3oo8CIccYqAA0K94O7sxMaG9U4UnAPDRwHjjeV1Tz3lB80PUuIHdGuR7Zc11OoavHmjnNIl7cuVqCqbhA8YALCT80CTetiz/cJ5lywnQqTi8NUBaB0+T3szbuLg3LuARMAugq8fm4pCpqE2Am1hsE7u/MMXtMGkWUHCqBukT3xp89PC9sxNBU2sMX+zHR5CbZmFrFu7+Ppgi0iVsFpzyypa8zFE0GdBHpm61DQJMROjP/spMnkB2NZoncFOCS6JVtUAuL6JvxYRxfkLhxFAVMkltY1ZutM8QPBntsaFDQJsQM19Wrk3ixn1bZllmiIN7cEmI8n9OXUPrCjbVIfuL4J/1rZgNil6a1G3sQ2hM5W1jD2mT1LQZMQO7CKw760gI5uet//+4+DWD/2z0PDsewgtz1wy5+L5dTeUpa8CVfXaxC5IA1p+XeRfb0M+/LuIPt6GQVSGxA6W3n3mdsG1/DVGkbUf2vKniXEDhSXccgUbPEe4e3pglB/D7PJQKOiA/HWM4/jXz+xXzMEAG55rJaz9E24EcBbu/TL+7XHAu62ZmmmM1vKqqY9x81PbrEks5xvNNIkxA6E+Xuybnu/qq7Vz36cMwKh/oazDSUAPp7QD6lTBmLC1pOc+xbQwc18Ix5o34T5YCrbmPDD1NmefGEAzN9zAWoNYzRT19b/1hQ0CbEDCzjU2jQ2Ivtxzgj8vHg0+nf1hl8HF4T7e+JfUwbg2qpkPNc3BABQ+tB8XdBWbLSTo/mbsLVMZRsT/hg725NPD6ob8M9jV7B0v+EkMVv/W1PQJMQOeLg6IaFnoNl25jbxe3u6YM/bw3Bu0Wj8MOcZJEQH6e1fDOzkyrlv9ytbj2yFkhQTjE9f7Qc+tlxSTVrbSIoJxom5I7BoTE/B7vHPH66ZPLXHlv/WFDQJsRP/mjoQvbuYPgB30ZieVm3i3z39Kc6PsXV5uuTeIfh4Yn/eno9q0grPSSrBH4eG8za93pKaZSKtLf6tKWgSYkf2z4zHpvF94OFi+H/N5YcKrVq78evIfaQpRnm65N7BmDUygpfnopq0tqGdXhezLpMt/q0paBJiZ57v3wUf/qGvwWtiJLiIVZ4u/LEOVj1eAqpJa2vaNU4fTxeb39tW/9YUNAmxM2oNg+WHDO+l5CPpYfqwUNZtxXyD4GPUQDVpbS8pJhi5C0fhLwkR8G5RetHYDAofbPVvTUGTEDtjrjKOtUkP7yexz1A9aIPyecYMCveDm7Nlb1F+HVz09vcR23KSShAp6wQPV/2g6e3hilHRgbwkejU3a2SEzf6tqbgBIXaGbTKDpUkPb+/KZd022kxikpCcpBI81zsY/z13h9Pj/Dq44tT8kXC1MOAS62n3VLacC7lXUYt7BbXYNLEfSitqkXX1V+TeeoCHtWqr7jcwzHZT8BQ0CbEzbKclLZm+rKlXI6OglFXb4jVjOD8/334XGcg5aK56MYYCpohMFd5n0LTWvCqtECfmjkBKfHe9A6x93F3wxy/OgOG48vDu1+ew9oXe+F2vIMGnaOkvixA7o62MY+x/fWsSXNjWuJ08pBvn5+ZburwE73x13nxDDsSuW9oecF1ecJJKENfDHy/07YynowIxPT6c8z3vVzYgZWcuIj5IEzxJjoImIXbGVHky7feWJj2wrXHLqRauACw9q1EC40lS6fISDFt7HBNTT+Hd3XmYmHoKw9Yep1J7PLN2eWF+cjR6BVt2lqaGAd4QOLucgiYhdshYeTKZt7tVCS5sa9xyqYUrBEvPajSWJGUvdUvbAz6WF17q38WqPizc87Ngswi0pkmInUqKCcaoaJluvSewU9OUrDVrNguSo7H91C1W7cRkbWWX5o9ns8a27EABRkXLaHsKD8ydfiJB04c/U8sLk+PCsDKtEJbGvfvVapwuUiKuh79lT2ACjTQJsWPN13vievhb/abu4eqEUdGma9yOig6Eh6tTq5/XN2qwLesGFu+TY1vWDdQ3CndIsLV7NJs/XugtPEQfH8sLrs5STLNgbbM5oUrq0UjTQTTPMONjxEHar9QpAzHtyzMGs2hHRQcidcrAVj9fnVaA1KwivU/+K9MKMS0+HPMFGJVac1ajj6eL3ihG6C08pDXt8kLLsy9lHM6+1P5dtfy7Y0uoknoUNB2APRy8StqW1CkDUVOvxqq0AhSXVSPM3xMLkqMNjjBXpxXgs8zWB1drGOh+znfg1I5W3txxDhK0OnfbpNefCm9xsotwW3iIcXwsL8xPjsbs0VHYnl2Mm8pqBHVyxfrvr5p9XICnk2Al9SQMw3VHTPtRUVEBb29vqFQqeHmJs8nb2CZh7Z8dVT0hQqqpVyN6SbrJfXNSCXBp+bOC7I009IHRFB9PF+QuHKX3xqzWMBi29rjZNbYTc0fQ7I0DeHr9cdwsqzHZZgvH90Uu7/W0pmnHzCUwAHTILuFPyz2Mafl3MWhVhtmN5hoG2J5dLEiftGc1fjVtCDZO6Iu/JDSdfGIstK15KbZV4BNyCw+xvR/njMBjFpzWwxeanrVjXBIYhMgSI+0H1xFdSzeVwu3r1CZDaUXKOnFeruBjjY2Ir75Rgy9OFqGq3njZPaGzoSlo2jFKYCC2YGwJgItQP9vt67R0rUyILTzEdgwloxki9GCCgqYdowQGIjRLK+80J5U07auzpZajT6EfR8RlLBnNFKEGE7SmaceErEFKCGB55Z3mpsWHU4F0Ipj6Rg1Ss7gFTEC4wQT9pdsxSmAgQrPm07hUAswYLsw+TUK0tmcXc9qnKfRggoKmnROqBikhgOWfxju5O+HisiQKmERwXJLMbDGYoDVNB0AJDEQollbeWf/7PgYLIRDCNy5JZrbIhqbiBibYQ3EDQoSmzZ4FzFfekUqAjyf2Q3LvEOE7Rgia1jSjFh02OUUrAfDlnwbhqccDLBpMUHEDQghrxpYADPl4Yn8KmMQiNfVqLNp7AZO35WDR3guoMbHXsjk2xdunDw9H/BOP2WT2jUaaJtBIk7Qn2kMBjhYo8F3eHSirGnTXqNYxsQbXAwIMMbRPUyoBL4cGcHmvp6BpAgVN0l7RqTqEL8YCphaXwFnfqNEVbw/188TkuDBetjtxea+nRCBCSCtUBIDwoaZebTJgAkBGQSlq6tWsEstcnaVIie8OtYbBqetl2HTsCoCmv9Uh3a0/b5YNCpqEEEIEsSqtgHW75eNiWbVNl5dg3p4LKK9+tHzw8Q/X4OPpgjUvxQq+hECJQIQQQgRRXMZujyXbdunyEryx45xewNQqr27AGzvOIV1ewqmPXFHQJIQQIogwf3Z7LNm0U2sYLN1/0Ww7oY9LpKBJCCFEEAtYZrWyaXe6SAlFRZ3ZdtoTToRCQZMQQoggPFydMCo60GSbUdGBrJKAuNRJFvK4RAqahBBCBJM6ZaDRwMlluwmXOslCHpdI2bOEEEIElTplIGrq1ViVVoDismqE+XtiQXI0p/rFg8L9IPNyMztFK/RxiRQ0CSGECM7D1Yn1thJDnKQSLH2+F974rU6yMUIfl0jTs4QQQhxCUkwwtrzWHz6eLq2u+Xq6YIsNjkukkSYhhBCHoT0q8dT1MmTfuA9bVwQSZKR5584dvPbaa/D394eHhwdiY2Nx9uxZ3XWGYbB48WIEBwfDw8MDCQkJuHr1qt5zKJVKTJo0CV5eXvDx8UFKSgoqKyv12uTn5yM+Ph7u7u7o2rUr1q1b16ov3377LaKiouDu7o7Y2FikpaUJ8SsTQgixESepBEMjAvB+YhTeT4zEUAuPBLME70HzwYMHGDp0KFxcXHD48GEUFBTgww8/hK+vr67NunXrsGnTJmzZsgU5OTno0KEDEhMTUVv7KE140qRJuHjxIjIyMnDw4EFkZmZi+vTpuusVFRUYPXo0QkNDkZubi/Xr12Pp0qXYunWrrs3JkycxceJEpKSk4Pz58xg3bhzGjRsHuVzO969NCCGkPWB4NnfuXGbYsGFGr2s0GkYmkzHr16/X/ay8vJxxc3NjvvrqK4ZhGKagoIABwJw5c0bX5vDhw4xEImHu3LnDMAzDfPrpp4yvry9TV1end+/IyEjd9+PHj2fGjBmjd//BgwczM2bMYPW7qFQqBgCjUqlYtSeEEOJ4uLzX8z7S3L9/PwYMGIA//OEPCAwMRL9+/ZCamqq7XlRUBIVCgYSEBN3PvL29MXjwYGRnZwMAsrOz4ePjgwEDBujaJCQkQCqVIicnR9dm+PDhcHV11bVJTEzE5cuX8eDBA12b5vfRttHehxBCCOGC96B548YNbN68GREREThy5AjefPNNvPPOO/jiiy8AAAqFAgAQFBSk97igoCDdNYVCgcBA/c2wzs7O8PPz02tj6Dma38NYG+31lurq6lBRUaH3RQghhGjxnj2r0WgwYMAArFq1CgDQr18/yOVybNmyBVOnTuX7drxavXo1li1bJnY3CCGE2CneR5rBwcGIjtYvvtuzZ0/cunULACCTyQAA9+7d02tz79493TWZTIbSUv2DSxsbG6FUKvXaGHqO5vcw1kZ7vaX58+dDpVLpvn755Rd2vzQhhJB2gfegOXToUFy+fFnvZ1euXEFoaCgAIDw8HDKZDMeOHdNdr6ioQE5ODuLi4gAAcXFxKC8vR25urq7N8ePHodFoMHjwYF2bzMxMNDQ8OlctIyMDkZGRukzduLg4vfto22jv05Kbmxu8vLz0vgghhBAdvrOQTp8+zTg7OzMrV65krl69yuzcuZPx9PRkduzYoWuzZs0axsfHh9m3bx+Tn5/PvPDCC0x4eDhTU1Oja5OUlMT069ePycnJYU6cOMFEREQwEydO1F0vLy9ngoKCmMmTJzNyuZzZvXs34+npyXz22We6Nj/99BPj7OzM/P3vf2cKCwuZJUuWMC4uLsyFCxdY/S6UPUsIIW0fl/d63oMmwzDMgQMHmJiYGMbNzY2Jiopitm7dqnddo9EwixYtYoKCghg3Nzdm5MiRzOXLl/XalJWVMRMnTmQ6duzIeHl5Ma+//jrz8OFDvTY///wzM2zYMMbNzY3p3Lkzs2bNmlZ9+eabb5gnnniCcXV1ZXr16sUcOnSI9e9BQZMQQto+Lu/1EoZhhDvi2sGpVCr4+Pjgl19+oalaQghpoyoqKtC1a1eUl5fD29vbZFuqPWvCw4cPAQBdu3YVuSeEEEKE9vDhQ7NBk0aaJmg0Gty9exedOnWCRGKbuoZC0n6aopGzefRacUOvF3v0WnFji9eLYRg8fPgQISEhkEpN58fSSNMEqVSKLl26iN0N3lFmMHv0WnFDrxd79FpxI/TrZW6EqUXnaRJCCCEsUdAkhBBCWKKg2Y64ublhyZIlcHNzE7srdo9eK27o9WKPXitu7O31okQgQgghhCUaaRJCCCEsUdAkhBBCWKKgSQghhLBEQZMQQghhiYKmnbtz5w5ee+01+Pv7w8PDA7GxsTh79qzuOsMwWLx4MYKDg+Hh4YGEhARcvXpV7zmUSiUmTZoELy8v+Pj4ICUlBZWVlXpt8vPzER8fD3d3d3Tt2hXr1q1r1Zdvv/0WUVFRcHd3R2xsLNLS0oT5pS2gVquxaNEihIeHw8PDAz169MDy5cvRPM+tPb9WmZmZGDt2LEJCQiCRSLB371696/b02rDpi5BMvVYNDQ2YO3cuYmNj0aFDB4SEhGDKlCm4e/eu3nO0l9cKMP+31dwbb7wBiUSCjz76SO/nDvV6CVIynvBCqVQyoaGhzB//+EcmJyeHuXHjBnPkyBHm2rVrujZr1qxhvL29mb179zI///wz8/zzzxs8Zq1Pnz7MqVOnmKysLObxxx/XO2ZNpVIxQUFBzKRJkxi5XM589dVXjIeHR6tj1pycnJh169YxBQUFzMKFCzkdsya0lStXMv7+/szBgweZoqIi5ttvv2U6duzIbNy4UdemPb9WaWlpzAcffMDs2bOHAcB89913etft6bVh0xchmXqtysvLmYSEBObrr79mLl26xGRnZzODBg1innzySb3naC+vFcOY/9vS2rNnD9OnTx8mJCSE2bBhg941R3q9KGjasblz5zLDhg0zel2j0TAymYxZv3697mfl5eWMm5sb89VXXzEMwzAFBQUMAObMmTO6NocPH2YkEglz584dhmEY5tNPP2V8fX2Zuro6vXtHRkbqvh8/fjwzZswYvfsPHjyYmTFjhnW/JE/GjBnD/OlPf9L72UsvvcRMmjSJYRh6rZpr+cZmT68Nm77YkqkgoHX69GkGAHPz5k2GYdrva8Uwxl+v27dvM507d2bkcjkTGhqqFzQd7fWi6Vk7tn//fgwYMAB/+MMfEBgYiH79+iE1NVV3vaioCAqFAgkJCbqfeXt7Y/DgwcjOzgYAZGdnw8fHBwMGDNC1SUhIgFQqRU5Ojq7N8OHD4erqqmuTmJiIy5cv48GDB7o2ze+jbaO9j9ieeuopHDt2DFeuXAEA/Pzzzzhx4gSeffZZAPRamWJPrw2bvtgblUoFiUQCHx8fAPRataTRaDB58mTMmTMHvXr1anXd0V4vCpp27MaNG9i8eTMiIiJw5MgRvPnmm3jnnXfwxRdfAAAUCgUAICgoSO9xQUFBumsKhQKBgYF6152dneHn56fXxtBzNL+HsTba62KbN28eJkyYgKioKLi4uKBfv36YNWsWJk2aBIBeK1Ps6bVh0xd7Ultbi7lz52LixIm6YuL0Wulbu3YtnJ2d8c477xi87mivF51yYsc0Gg0GDBiAVatWAQD69esHuVyOLVu2YOrUqSL3zr5888032LlzJ3bt2oVevXohLy8Ps2bNQkhICL1WRBANDQ0YP348GIbB5s2bxe6OXcrNzcXGjRtx7ty5NnG8IkAjTbsWHByM6OhovZ/17NkTt27dAgDIZDIAwL179/Ta3Lt3T3dNJpOhtLRU73pjYyOUSqVeG0PP0fwextpor4ttzpw5utFmbGwsJk+ejL/85S9YvXo1AHqtTLGn14ZNX+yBNmDevHkTGRkZekdW0Wv1SFZWFkpLS9GtWzc4OzvD2dkZN2/exOzZsxEWFgbA8V4vCpp2bOjQobh8+bLez65cuYLQ0FAAQHh4OGQyGY4dO6a7XlFRgZycHMTFxQEA4uLiUF5ejtzcXF2b48ePQ6PRYPDgwbo2mZmZaGho0LXJyMhAZGQkfH19dW2a30fbRnsfsVVXV7c6PNbJyQkajQYAvVam2NNrw6YvYtMGzKtXr+Lo0aPw9/fXu06v1SOTJ09Gfn4+8vLydF8hISGYM2cOjhw5AsABXy/WKUPE5k6fPs04OzszK1euZK5evcrs3LmT8fT0ZHbs2KFrs2bNGsbHx4fZt28fk5+fz7zwwgsGtwr069ePycnJYU6cOMFERETopXOXl5czQUFBzOTJkxm5XM7s3r2b8fT0bJXO7ezszPz9739nCgsLmSVLloi+jaK5qVOnMp07d9ZtOdmzZw8TEBDA/PWvf9W1ac+v1cOHD5nz588z58+fZwAw//jHP5jz58/rMj7t6bVh0xchmXqt6uvrmeeff57p0qULk5eXx5SUlOi+mmd2tpfXimHM/2211DJ7lmEc6/WioGnnDhw4wMTExDBubm5MVFQUs3XrVr3rGo2GWbRoERMUFMS4ubkxI0eOZC5fvqzXpqysjJk4cSLTsWNHxsvLi3n99deZhw8f6rX5+eefmWHDhjFubm5M586dmTVr1rTqyzfffMM88cQTjKurK9OrVy/m0KFD/P/CFqqoqGDeffddplu3boy7uzvTvXt35oMPPtB7I2vPr9UPP/zAAGj1NXXqVIZh7Ou1YdMXIZl6rYqKigxeA8D88MMPuudoL68Vw5j/22rJUNB0pNeLjgYjhBBCWKI1TUIIIYQlCpqEEEIISxQ0CSGEEJYoaBJCCCEsUdAkhBBCWKKgSQghhLBEQZMQQghhiYImIYQQwhIFTUIIIYQlCpqEEEIISxQ0CSGEEJYoaBJCCCEs/X/JbWdCEMEbSQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aPK60isGlvHG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "d8066e41-10b1-4f6d-a77f-dcf6e3b3b14b"
      },
      "source": [
        "departamentos.loc[departamentos.l2==\"Capital Federal\"].plot()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qhFw80gn5B_k",
        "outputId": "1079bf51-97ac-4b30-84d7-03f884235a6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        }
      },
      "source": [
        "departamentos.loc[(departamentos.l2==\"Capital Federal\")&(departamentos.price<80000)].plot(column='price')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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jlZK5hRH4AeW1k7z7rz9D6ZWr2Dgy1nEu7/zxz7jv0INwrTYNx4CiH/D9O+/hh3fdx57JIr3ZDK885gje9KxjGSzk577YFCn2M6TGK8UTiv6efQiPKY22pvH7hAA0VjVGWQIEiNgYEFHb3pNHLZuPF7qUpg1pVRTeaIhbNJ2WI09QHbAJui3cksYbiZFKU+23sKsYgyYEmZGYyjwLZUNxwEEGmq4tPjIEGTdCjAIobDRzl9uGEEFY33b/TSX0TZvgLSthlnDmB6/5JZ99xUtmvL9rcpLXXPE9hiYq1Kqj9xZLfO2WP/Dfa+/n+3/3Wpb39859X1Ok2I+Qhg1TPKE48bgVdBcyHbcLAb25DCYo1wrLV4gY7EBj+xoratV811qjFvQhAkVmV7XuoTmTEV2bq7iTMUIZUoZd1XTtCOld75PdFWJFGr/HBiRBHvxuSWVQ4g9YyFhjlzUiBG8swq5qrHiG3rwJRUoJjtW6TWuk1iz7/nasYucatd9t2DzjvYmgzCu+/02G6qHWxshKa8bKFS666n87jpkixf6K1HileELhOjb/+Obntd1mVSLcPVXih0fJ7DWyUM25LqFmGoxmCCGM4RCC/JYymV1VRKjI7zAGo/nY2v+tUBP0WlTmOSANEzHOSuKMAFnTpRJoG6TSZIej2SOSWqO6W71LkVyDXQmxx2N03D4fF7fRC/jY3T9hbG9nQaxYa+7buYcHdu1puz1Fiv0VqfFK8YTj7BccxQfOfwk93Q26nzMZ4I0GWJFCSYEdalAxQRfErvlR9nTOYSs0xsBBEsbbUqb3wSmY2ZWr5Rh3KiLOGMMV5kTiQk07QiShyrn0aISANnkrARBrsnuqOJPtq5gPGuhreT1UneTGzevZlwTevTvT4rIUf1lIc14pnhSc9YIjeeFzn8Ed92zhwXu38cOv/hblGFqhALTSFHb4TLgefr9tvB9pkd8VzTquLE83DJLZlHwFRmVDWRoljFZhRwiBcgQymMUD1BoRdpijBHsqQnsO1lhA3Oe2bP7cK85qef3gxHYY3Te5Dlu2J3qkSLG/IvW8UvzR0FqzZfcY92/cxehkZ/p7JwyPFdmwbYhfXn0X2m4YLkh+a+jZ6FPYGiAijd8tid323pcpVAal1czt+6LlKwSyk9EBZKBwijFhbo4/GSEQne6FBuWa44VjY1UaFdGvOvYIlvb1tJ5TCArXjSL8mSr30/HsA5fPuU+KFPsTUs8rxR+FG9du4CtX3cyGHSOAWWhPP+4g3vma57J0Xm/bY3btmeDH/3MHv77xISanqkRKgQXe3mpSiNyKWq4otzckOxKj7OQNS4LVMGICUI7A77WRuQJdj0zUpaNEFKPdzgZHA2FBGs/OkshAo2pdlwERarJDIU61labf1pnTGooVRMVvey6hobyyy+TFJOT2aCoD8OYXnMh7Vj9nxv5HdC/Du7FEtz/ExKkLWkKZznAFayokztks6elicU8qHJ3iLwupqvwsSFXl2+PaWx7ko/9xnVG9iDCtQWywLEkh5/Gf//w3LJnXw11r1vO9r9/I1i3DKFsw5koUGqWaDECk8DrkgJqhZaOliQb8XgvlmVBZ7Ai00zBQVikiv6WIVY3RQNyTaWsca1/84gEeUUbijSuEEMQOhHmBUFDYHiBiZrAHiTSiWVtRgAoDnE17adeDWQPFg7spHzkfdwKkSrxFqaFHcOu/v6tl/1I14Ie/vouvfPc3qJwDEoIeCzkySf9vtuPtbnh3WsAznrWKL974Ue66fQOf+9drKU5VKRQyvPN9Z/GsU1fNeX9TpPhz4IlcM1PjNQtS4zUT5WrA6osuJxwLcIoKmahUaAFRThB3W5x+3MFEt+7igQd2JNsE/rysYe9NI0JIP8YpzZ7Hqo2BFKA1Wgimlnlgt/GotEbEGqeocMZ8RKgIuizcwNSFQcOjAygvdAl6Lawqde9KA1FWYJdjvPF41vxWpc9CW4LIlUQ5GLhlL9Z4ERnppiJoKB7STbRkHkK1GtHaGZe4Gd7+t6fzghcdxXixwls++yM27R5FK90omt4xQf8Nj5rwapu/WneJjT//YBidhGLF3K/5vRQGuvnR/16IbaeBlhSPL1Lj9RRBarxm4qe/f4BLvvi/OCU9I3SmAeVAtV/Sd98UItbGk/Eswr72tV0yiHGK+2C8pKifTyuFPGqAqWqAav76JoZNBjGZkQiZeExKQnlAYsXgTMV1OrzfY7w3GWhkqFG2QGiNVTUncsdCrFkaMWswGok5Gw0EeZBKoCsVEDH2ZIByLSrLC7glCysEGbbzyowL5+3xmd+VY/lLDuLG+zYSK9O6xds0RkxA/oFhnNFqZ2MqBGQ8o+7R/J7r4DxjGdfenHZ3TvH44olcM9NHsRSPCfc9tB2n1KCjN0NgFme7ArErcKrmfcMi1DO8rtq2WciAQKPWK84IRk6AqcM9RFRkcKsLm0z4DgV2KUJEGrfcOj+pID+kUDZEeQu/x0JLExa0qopqj0Rlmrw4pcmMa7zRue+HiKmHD90ilOeDpzOEtiIWCtWVQ1gOYbdp8yVijTupsZvSYiIpaCsvyzC0vcT6tRsSK60p3LoVe/MYw399MH2/2z77ZGr3uEbVj2NQCspVwjsf5UXZN2FZklNfdjwf/M7/xeogRZUixdMBKdswxWPC1vXDs9ZaATglhZybIGcgBHF2jkVUmHCj7YMVCLQAZ1ygt8XJe2BFoC05w3DVh8Ds400ossMxmQmF7WvK8yTKm7azFFT7JcXF+/hsF2sjCwW4JU0U+xRu2Uw8vwedc1p21RL8Pkk0zRHVGObh5IHZ+uS9reNkN4wy8upVWOU5ukfXEEUIKc2P44Dj1I2ZAFSsuPnqO/ibA/+JON7XDylFiqceUuOV4jHBUrN7SQLjjVhhw8TJIG7rddUQe5LIlXUPq+UnYT/Ujh64Q5PbAv23N5pV1ucWqPocZoMMFQhBtVe2FCTLUOONKvLbY7o3RngTmsiVpv6r6XiNCUUqyxBOMsM+ud1VvKEqmT0hvdeuo3jy0s7Fzlrjd7VS/oU287dLDfX67MNDJpc4mEVl99GQyuSapMkRCksiXBtcx2xLMLZ3ks+e9+/7NmaKFE9BpMYrxWPCYF9hbuugFNVeG7/bppoNCZwSyp+coQZfR7LgBtlpX0dR2yZa3hv4Q827mkb+2EdHQoamRi3KUR/bqmpyuzXuhCYzEeOWjWcmNGBLlC1QkUIrRZSTRAWbOGcRFWz8QQ+/10FEmsxkRPSMJcS92c4GWwiwBLHX+p5yjC6iO6URlQh7tGyaYwqoLC8QFZzZFUZqIcPaeROPFSHAthCug+5uKNDf+OPb9u2GpUjxFERqvFI8Jjz/uYd1aKwFkauJHCOai6qix3fg3bOD7F17yN26DblpAyocbRix5Lf0DaXdq7SyI4QG4ml9tBJR3ZnE95kdlzvBqGo0eYNKkx02LEW3FNevr4XyLgQ6YxF2O6Zrc/JebQzlSoI+18hNWTms6ixMj+TatWx9bVcUVlWhpcaOJOUeE+6TFRMKHHv+0ham5Izr8lyj79g0r9o8BYb0IiJFfOhSAOIwDRumePoiNV4pHhOeffIqVi4fnOFF+V0YZtyEIqKIe98OvA2VFkq3s90nu2YXKhhCRAoZKOypEJTC9hshvxl1Us3nEqCEaNucMijIOYkfYVagZRLWrKnOl8xGe5awoymEbg0ztmyvhFjDk+iRMZgskR2ao3ZNiEYzTK2RZYXla+yywhtXyEgTn3wYSkDvdZvRaEpH9jN89gHopL6tZqyVK6kc0oeYjQqfNMhEa1AQL5+/b8ojKVI8RZGyDVM8JtiW5OPveznnvu2bKAvQmtiBoNuib495kpcjw4hAt3YqxnhSGvAeGCFeWcCWDsoCu6I6rqMJ6a7OpBMaygttooJERBq7qrFLGqE0OisIMyLxzFpRYzRWB21yeyJkqHEnFEGPxAoSDzBsr1lYKwFQFsYANCOI8Nbtxh4rtyh+5HePUH3xCqKBDv3LlMaqmhtiVVXLU6TGkF7CrGTP3x1tjJWO0UJSPHqQ0hEDZNePY00GxAUHnXFZeN3ODneweWBDixR+iFrQT3Zscu5jUqR4iiL1vFI8Zlzxb7/AHalgVYwR8HslmRET+oso4W2ozjBcNQjAmlJE+SmAmU0nOxwDJLVUkihvqPcyULjjEW4pxqkosqMxSEGUaShxNHtoxUU2YcGiuMCmPN/CKRmZi7oHl/xHkRAyBPjdgvGDbcYOdRstUmqIFdl7t2ONlevzrO0hlGbw55uQpfYsQXdKm75kFYWc5kLWxrADRW5YkRmOcMcg7I6Js4YxU1nVi7+yl9zukN67hhl6bu8cd7EJtgWW5IVvPXPfj0mR4imG1PNK8Ziwe8cYt9zwIGiQlkRnHaKcxBszShRazE3p1gJ0HBNlJKh9r5EPuyRTyzysQCMDTXZoZs7GSDkJyvMtZGhCZMoTprbLMnVWlpbEnkTZpjg59sAtQ2xB2G0R5USdFUiMkcDCeF8yaDIueycR5aB9mFEItNZ0376L8TMOaLp4jTca45Y0IuocuauxNgs7QoSG7k0wuddm9ARJdamGWBPnNZUVvYi4l4Xf39YIr85GFFEa1WtIG6e9+uQ57niKFE9dpMYrxWPCA3dtqau/W1M+2o9AOUmxccw+faU0YAuijI09uQ/GDgjtGGdPFWueg7YF3oQxXO3quTTgTCmmljszvCXLT0KECqyyJjcKciok6LWpzrfREiPhFDfYjLKsUYHC7xJkRxrG1t4zd9gts7NEdjhGWQItjYpH13afyJHYpQgrMNehXIso79TzWdMhFfRsiciOWJQXW4RdAqkdsjsCdp1lMfxX88l/aXsLHb71JhpFFNWdQ7um9uyII5fMOf8UKZ6qSMOGKR4TmtXEBKaGK7PXp9pvyBJWnCOaZ89eyGxD7PWCJZAII6zb6XyACGO84YDq4hzaSZQxgtm9FqlIwoK6/mNVdQudXtuSyIK4YKMcU0AsYpFoIDZfp6hLO8VeY4sIZtE9hDpBwvbBKWusEOwAwozEG/GxqjFCgVAgqzHeSBWr2CB6KKsR0wy6LIrLM/jzXKzQIjNqkdkSEnUJtAWVFRnGn9mFVjWxyVZGp8bIaqkVCwDo7sumWocpntZIv70pHhOOOO6AVmVbIL+tSvGADH63wJ20CA7sJTc03Fb2SQOVY7uxbRO60kohQ2VU46cVQGtAxDFUArQUVOd7hrQRz0FDxxhZZyom9qQxDhEtzEcNaAviHklYuxgt6kLDM2YuBFILYkcRuyZnpTIOohp27tKsdZ1WXyOeuKMhmb1+W48RTEdp5UqUJykthcwo+N0W/oDLdKguD2KwJyKiXtj9+gWEAw79N4xhheYqtYaoIIjCAGvhEmNQJXznP9825z38c6Fc8Xnbh7/N2nCC2BNYvuZYp4fLP34uuex0eZMUKfYNqfFK8ZiwaFk/J51+KH/43aOoxIjYFUXfPUXGji5Q2BGRHR2k/CxN5p5R8DVYGBaEhMox3ajCIuPy68QTkkbCIixYOOUYEWt0qYwYm0JWjWnROQ9nr0e4sGAM3RwQgDsREeVsE19oygNpQDXZAhGA9gQioq3BbRyoEVqgbCgtENg9HvZ45yacQgjC+d1J6xPzXnZ3ZdZ5a8AqhQT9HtKPUGPj+Acs7HQCQFPYYDF+giGrjLx4gNEX9JHZ5iNiTXWxh8pbHPDvE+hsgWzO5sdXXkgm88T86W/ctJuXX/ZfhD22eVoQgiivuU2UeNbFX+La889lxQHzn5C5pNi/kIYNUzxmXPTJV7F0xSDQsAn54ZDB2ydRDowfaOMvWkjp1IOoPGuAyrE9VJ/ZT/X45dC/HOm4xnD5Cqk044dmGT0iR5SRVOZ7RKVJrF2jiKoJoQlAlH16bt1O9tERtA2x3blYF0j6mijsqgkdiiBGBDFobZpaJgOLpB8Z0NKfqy2E0anSyoQPp46Zh7IF7RozaG1aovir5hsPzzNyTdO9yxmnAESs2HOyQBdjdr18IROrFBMHKyrztOn/1bK/wBuTyCahX+1IKgdmKa/KobISZwT8pb28cPUh/Oyn73nCDBfAKz/3n4TdSR6vWfkDQ4455zNXPGFzSbF/IfW8Ujxm9PTl+dIP3871P13LL358J6PDU8xf1MOLX3Uio8Uy3/rGTajEUCB6UbYkytqojNVg8WlwpkKq/Ya+DjC10mPw/imcXYYI0bzI1/6ff2CIYH6eoNclMxx39JTsiSoIjbe7TGG8YvQMgThjUV6Wp7ooC0rUm0ISNXJDs5H3Q9PXEqcMwrYYXX0wPTdtxp4M6jVgWmtU3qV44nJUzm6QRlT7OrLpUC6IQEBvlv6HYtNmRYWEosLYUS750SxOaZo0VpWZAsMJk9PZDW9/8XGc+/cv3Iez//nw69/eS2W+PSv7sTLf5oab7uf5zz3yCZ1biqc/0n5esyDt5/XH45fXreUzn/wZSiuigoPKNgyXrMbYxQihoDLfprjcrLoCGPj1Fuy95c55JAHVA3ooHbsIq6LwxuK6ARIASmNPVLEmqmAJ420l26KsTdjjICNN6AqqK3vqXZK10mhXICIjPVXLiema0xABsSYqJPmz6bqKpQB3+wTKAt3fjcpPsyRKY035dG33Z3UZNZrqgI3OZRHT28hojSj7lPUoLF+CjBuBE7+vyvix0sRSNCAFVlmzdJ3gZ/9yPoV8h2LpxxHn/NOXeWje3F2yjxh2+MkX/+8TMKMUjzfSfl4pnvZYtLgfACkkbjFCT4VoYZo91vT3TB6oNRclJ2aSGZohNDijJm8UZwTlhRaWr3EnIjK7K1hlUxdlKpqN4QrzNhNH9BHnG6K1ItbYU1Ei+SRRbo0IIlBCm1qvpHMzAJ6A2LRVaQeVd6keOi8hmYC2a+xFsHwQUpDZMIzOdCXz0zM9kuRcQjrttwuBznrkd2eobtqFXr6k7sUO3KsZuDdgaoVEe4KlmW6+cNHrOeDtg7PczccXsZ6bWAOYppspUjxGpMYrxeOCZxy+tPEi0dUTyf+h4XxoaeSR4lozyH0gY9iTAd237SCa3wXZDM5UjDNcQqhG6K9mCKKczdgzB42hbCZtSAh7HayqInIBAVKDO+xTXpppkspoHFOTt5orZ+X3mcLo+nuRprBuCmfXFCzOGLFhz201UMl8ldDo2XJSUqAW9pG/+UEqTp54QbfxYAds8nftwcr3867zX8arVh8/U8rqCcZZJzyDR7bdP/tOWvPSk454YiaUYr9CSthI8bjAtiUHrlrQNkKmAG0LlCvJjsX0rfPpfbSKXVZUlxTmbHaJEHh7y+Qe2Is3EiDLodE2TDZr2wjoCqB4aM8Mw1UbAxIihTC1Vj0bq6ZIuIP47r4K2eppf1XagqnDu6guKxDZGhHGiFIVggii2PwEkXnPbvL2OkEKyLgUbt5I5tb1oGK0Z1NckeVdH30lr37xCU+64QJ4+9+9EGci7nw9WuNMxvzDG5//xE4sxX6B1HileNzw+cv+loxnt3hCsS1QOQvlWYkBs1CexPKhd72Pv6Rr7oFroT+tEZUAYmXGydiEvVmigTwIgZIQ9Dp1WSQZaCzf/Baxbng+QuCNhkansa8zwWC6UZqxnUT6arrjlIw3fsoCrHJkbKPWCD9AjE7A9l2wbSd6zxC6MjuVvj5k4mV6u0q4921Ba03liPn0r5/ap+OfKHz0uachQhrF4jT+L0LNJ5733Cd1fimevkiNV4rHDbmcy49+eiEnnXIwQkpii1b5o+YwniPRUpAt28R9s5ALpGzxKjSgerJE/Xni3hw6Y4yVyrnEnrEiMtTY1YRoEZvftm9yUbUFNTNmklnKmsVjEcIYp06bgTDfuC6NRndHqBUV4lUVgmM01YOMNJPWGj0+iZ6YRIeRmYdSyF1jnc9fQxgRdFsEB/RA1iW7vYK9cRfZdSN8+xM/ZnTPeNvDlNq3HNSfE69++cl846UvoX9nbIyY0ogQ+nfGfOOlZ/OKl570hM8pxf6Bx8w2vOmmm/jMZz7DnXfeya5du7jqqqs455xz6tu11nzkIx/h61//OuPj4zz72c/mq1/9KqtWrQLgt7/9LWeccUbbsW+//XZOPPFENm/ezMqVK2dsX7NmDSef3BATvfLKK7n44ovZvHkzq1at4tOf/jRnnXXWPs9lLqRswz8dUazQWrNrcopz3/YN9GSEaNc1MtHek4FCVCP0yF7c8WlMtVpPqtohQLhyIbg2IlSGcCEFyrEAjTU8ycgpC7E7EN5qxcNxRtD3UAkr1IweljX5t05hN2Xar0gl0NPMWJQVRInx0lKjVlWgOzZx0poqiYSu3wV0XzOJtWeibSTSP3wpakFP2zloran2S6qDyUNApMivG6Xntl0QGyq+tgXWqn6e/ZKTOfnUQ7nuu2t44LaN6OT+HHjUUv7po6/g0COXzhg/RYo/BU/kmvmYPa9SqcQxxxzDZZdd1nb7pZdeype+9CUuv/xybrvtNvL5PKtXr6ZarQJw6qmnsmvXrpaft7zlLaxcuZJnPvOZLWP9+te/btnvhBNOqG+75ZZbeP3rX895553H3XffzTnnnMM555zD/fffv89zSfH4YGhokvd/6Epe/LLP8ey//RzHfugLvPCyb7HzKIvdJ7tMrLRML7AEWkDkmNVd24K42yFcuRiVtY2nVfuZZrh0zjOU8KkAWY2MAfNj7GKArMbEfQWsYLqJaaCmgYjSKEcQC8jujbDLqrPavRRkhxR2KUTZGmVrEBqrGhMVRN3gqJUV6EqEFGXtZObl1KkO/kqNsC3TnmQaScV9eAdyrGReqCTclswn6BZUB5r+bG1J6RkDjKxeUc/JiUijHhrh2vvX8PEvXsNtI9spL9KmmFppNtyzjQte9f+47caHO3+IKVI8xfEn1XkJIVo8L601ixcv5l3vehfvfve7AZiYmGDBggVcccUVvO51r5sxRhiGLFmyhPPPP5+LL74YoO553X333Rx77LFtz/3a176WUqnEtddeW3/v5JNP5thjj+Xyyy//o+YyHannNTeqfsjPb3yQn95wH3tHp/Biwej2CdCa4hKLyQPtmbRvrbHLmsF7o3ruyS61tjcRQCxinA27jXht0zYNaNeidPh88nvjlvebEeVtSivzs87fsNYVueG4pa+WFlDttfD7ZKOwWgicqZjsqFHtaG5eqQUMHeOaujBHIZeW0BZEfbRcu7s1YP5lI9hjcV2ZQwhhBHXDMNGHssBxUN1Z4vldaNsizkgmDysYgkkH9P9qM9nNDaV7LUC7ApkI98cFG0tmEJkMGnBzLlff/XFkJyX6FCkeI57Sntds2LRpE7t37+bMMxtN7np6ejjppJNYs2ZN22OuueYaRkZGePOb3zxj28tf/nLmz5/PaaedxjXXXNOybc2aNS3nAVi9enX9PH/MXHzfZ3JysuUnRWdMFqu89UPf5zPf+DXrNu5hbKTEyPYJNIbFN7lymixQDUIQ5QTFJZYR0C3F9UaOtR8AqS38QxYRDRbQlvmqalsSLuxh7OTFeJOqTpKIczZRf4ZoMEs0kCEuOHVSw2yQkSI/HLeI9oKhxWfGYrLDSZFzDN5oTCYxXNNlnoSGrs0hPeuqLL5uisWXK5Zcplh8mSK/1hxjjcUs+NwQVq2dS3MYVErwkmJtx0EA9lQVb8MQmXW7KS2yiJ02F1DzymJF6bD+Gdcg/caFyWIEk0X0VNHk58oBv/qfu+e8RylSPBXxZ63z2r17NwALFixoeX/BggX1bdPxzW9+k9WrV7N0aSP+XigU+NznPsezn/1spJT8+Mc/5pxzzuHqq6/m5S9/ef1cs53nj5nLJZdcwsc+9rF9vdy/SOyYmORH993HprFxtty8i5FtxsBrwPYNIUAA5QVzPBcJQXGJpOfhmQ0l67sAdiTxDxgkXNHwfmIHoh6Jvd43IcdeEz5s1s7TnoUWllnYZ6kdsyumeKvdHgLwJhXSD5BxUqemtfEE42nWTmsKm6qIuFUCyh6DwZ9q7HEQY0Wkr9t2ma6lxBjoRZeqyGkGP8q39iYTsSYzovDGFTJO8n8ZD5V1kZVGjzRtCYpHLyJc1gtCggZrokx23QhuZHP3mvWsfsUJpEjxdMOTWqS8fft2fvGLX/CjH/2o5f3BwUEuuuii+usTTzyRnTt38pnPfKZuvB4PfOADH2g57+TkJMuWLXvczvd0w7/f/gc+c9PvEEIgA03/ptaFWkaN11F2H+qMLEFlnktub4Ds4CQJjHekXYswbxF7gigDIkq0CmuLehvvTmiNXVGEedler1Ap7IqatXxLY3qHKVvjTISIUKEKzozzyUo0w3DV5g/Q+ztNXCy3NVwtCGPoykOxVa3eqsZGJ1EYCavuLVFLV2cBOFWBf+xKnEd2orM2sWtRPGoeVvJnLpJ/4t48xZNy5B7ay6P3b51jQilSPDXxZw0bLlxoWjfs2bOn5f09e/bUtzXjW9/6FgMDA/tkkE466STWr1/fcq7ZzvNY5wLgeR7d3d0tPykMrnnoYS696XeGmq411tTsIrOi5lBpjTOpyO5VZIYVImpOLGksJQgGPNRs30QpKC6y8XslUVagbEGckSiZUO9nEX51izEybKoxajp3J49rOrSE6oBFedDGqoTISjRjPFntoBtVG0OALM1uuYxnp6CQm5G/y2+YqP8/tzduMVyN442kVfCMpfiHLqR43MKG4UpydJavsH2FjKB86Dw27hnjBav+iamp0qxzS5HiqYY/q/FauXIlCxcu5Prrr6+/Nzk5yW233cYpp5zSsq/Wmm9961v87d/+LY7TLpjfirVr17Jo0aL661NOOaXlPAC/+tWv6ud5LHNJMTu01ly25tY5F3plNUgT2WGFU9QM3hMx8GBMz8aY3vUx8++MKGyNQSm8MaNzqKWgtCRDaaHdthC40i8bqhdCGDUNR+IPzPSA2kyezNg0BqHWiMioO+wLhAa7IhC2jfIkshggEpV6jabaLWeQSmbOA3R29g7TGsCx2zIQc+snsKeM5+dOzvLgkMhw6SR7aLQWNXYlIZjEid5ioHGqEC7pRi5YwCuf9yne9fnvEj8JtWApUvwxeMxhw2Kx2OIBbdq0ibVr19Lf38/y5cu54IIL+OQnP8mqVatYuXIlF198MYsXL26pBQO44YYb2LRpE295y1tmnOPb3/42ruty3HHHAfCTn/yE//iP/+Ab3/hGfZ93vvOdnH766Xzuc5/j7LPP5gc/+AF33HEHX/va1wCTDN/XuaSYHTsnp1g/OtryXuQxg0UYexIrCee5wxH9ZWZQAIWG/E6FiAS2nyhlYIqGJw50KC2xGbi3ipUQ77QFUbbB+JMROFMKbWuiLgd7cnZShtCAAreksMsKLcxrHCNPpcNGnq7t8UDkyrouY2VhjvzWItZ4FeVZlFbkzTiCGaSP6QOFy/Nk7hufbRd0odZhemZIdvC6LYyfvAShZ3/Y08lgMjJKHFZC2ph+jRrQtoUmQmQ87v3+I5y26xJ++dF30ZXLzHqOFCmebDxm43XHHXe0FBnXckTnnnsuV1xxBe9973splUq89a1vZXx8nNNOO43rrruOTKb1j+Gb3/wmp556Kocddljb83ziE59gy5Yt2LbNYYcdxg9/+ENe/epX17efeuqpfO973+NDH/oQH/zgB1m1ahVXX301Rx7Z6Au0r3NJMTuq0cyQWHYkxqpo4mwjbKdtSeRpnKnIdDtWuq3GngByezXaoSH1BNhVCAqS8UM9+u/3k8aM0PdIleqAQ5y1sINEHDcArSWaeE6PMLYh6LHQTuLWKY1VVQgNkQtO0P64mi1SbuMMKmNTPKALb6SCtgQqUQwJehzc8bCzEdQgRA9xt4+crMyg/gtA53PgOhBF9XYozZUsViWmb80u/KOXz3HFDcioveGqvacBP6sJ+zNI38ZZU+H0t32B/3zvGzjyyH0/T4oUTzTSfl6zIK3zMqiGISd+5XLKYUOqomd9RO8DIWG3g1SJVqCE2BG4wz5RnzdrSM94VQKa5JiKiwVBtzEw8/5Qxqk0vpph3iIqzNQdtMoxVtThK6w1Sgoq8x0zv1oblsSL0xL8HkHPoyGyTf5LY4wplpzxPgJUzQESAlmN6NpQaptH0xj5q2AwA0rjbRhCTJQQSYhOWxZ05yGfM2G/ig8TU+AHM+rjNFA9bgW4szR5BKKMwO+x8KbU7B6h1oiST2mZR2H9FLldPrEniSV4hSw/u+lDaR1Yin1G2s8rxZOOT3/qp/zyhgdRybPN/ILFjmMFYd4sZGEWsAVOYOSfhBDoGOxYofL7kIvCkAh0DCS088JWiF2F3yvwe22cijGWGlNw3FYuSegGHb75OUyYfE/QJXGKNUahNjVhrkA5ArTAroLfZ+GNx/W8VWyDcoTx1Dp4jqa+zPQAcyci4216NiIhbuimfZUrCfuTFsyWwD9oHu5QARlEZj+rlXQilIZc1hgvbbxbbUnTq0yBvWOU6MDWEpD6/cAQTIQQiLIPc4QYjSGHqGAxfmwvk4dEzP/dMF4Eqlxk9Ykf5tKvv4Xjjl8x+ziPI6rVkPvv3Ubgh6w8cD6LlvQ9aXNJ8dRB6nnNgr9Uz+slZ32Wahw3clpNX5HRwyTF5TbdGyL6Ho076u8pdxZ9QJpSYZpG+LB2nsTwWNUYeypCO4aR2A7SVzil2IzX7CAoCLskWsq2IbrYFcSeyVUFeU3X9rjeVbm0wMIpdc6DaUBZhobvVFR97iLSWH4MkUYoRexJVMZGu5Z5CNDJFI2sB5YfI/0YlEbb0oRFK7ExUpWA8jyL4kF5woGsuS1+TP6RMfL3D1F8yWK8ndnWiSWepXKT0oE9I8QL+pDIzqFVpbF3juPeu6WJ7AHCsaG/H5V1UHmPT3zhjZz67H3TBP1zQSnNd799M1d+/1Yq5UZs94QTV3Lh+85mwcKeJ3Q+KebGE7lmpsZrFvylGa9Sscqb3nQ5Y2W//Q7JV2XsDI+em32sDrkigNgSiaZf+2VTJ//optBbu3PJqkLEmqi7Q5hMa9yxaMbiHHmSODPLog0EeQlSUO0x5Ibc3gi7CuMrLPLDnY+rq4JMZxgmc7bLcd1KlgdNrq5uWGPwxiOTA5s+Ltp0ip4KmVhpU1mYMe8176k1OhMTH1ei739ishtstC0RShPrkGhhd/0+hQQU7tyBf8LKWe4CZG5eh5yoNBidCYQQMDiAjkLEIYv5yTUXku/Kdh7oz4wvfe7n/PSqu2a8Ly1Bb0+Or/zHeQwM7kMLnRRPGJ628lApnr646VcP8PrVn2WsWJ1ZE1VDQmuff6ea1XBBkwJFx0aENGjxbRs/JoE+R4IlkH4HCrcQxNmZOSnlti1LbtnHCnUjf+UKiksdJpfZoMDvbuzXfAyAXW1juJrmHGUs01G51yYu2A0jntD8W/JrWiP8GFmJkIFCowkLNuWFiVTU9LMIAb4F2z3IatzRCt7eEu5wGW84wN00An6Sm8y5BFmwNg+Z181yWcn/nQcTEWClTIfnKDa/k7kxMorKWMRjRf7l/G/Pckf/vNi8cait4QJQsWZ8osyPvnfrEzafFE89pDmvFNx39xb+5Z+vJHBn1hc1Q9lQ7bWpxppOHbdqYTkJ6FCjbNG6yjcVB+tO50rajmjZIFiIGLOoWjOft6KMNPJUlVrtFbNeR/N5lE2L8VSeQCMIHEXsQmasqeBaGK/L6tBixewjwIIwI00jzNp7CWxf1W+SLIfYxbCFUKEsQdTtmmJuu/M1yN0e9u7WxpNSCLwyOI+Mo2xN+aVZSmcvZ94P12OPlwlXDKJ6zCcnR4s4G/Zg7W2j36k1xDHashBKUc1Ctljhrt8/wsTIFD0D7b2ddXdu5NqvX88jd20ik/M49WUn8OJzT6fnj/COfvnze7EsQTxdhiuBijU/v3Yt/+f/noncl886xX6H1Hil4Gtf/jVBT2bWKluNMVwITAdkmbQTmbZP8xACsPxEPV2KxBgZMdp6S6/mMFWocMaqZkFPxotzNlHBBVsiA1AZbYgS9ZOavFzYbRF2W4YC39wlebYLAqLs9Lc1yoPu9RHlhTaleZAdo25wrQ4R1emIclbbOcjQnNgqh9hTM62giDXOmI83YeEPuG3HFghEBDLopnqAmZs15WNPVI1nJwQiFgSHCmIdsfd1h5F9dJTCPbtwpgKUZxN128iRIszvh4EeUxjtBzA0DmOT9bwcUuIOF9FdPSg/5MFbH+WUs4+fMafv/OtV/Ne/XIVlS+Kk1m/dnRu58vM/45Kfvo9Vx63YtxuXYHhoqqPTXkOlHOBXQ7K59vcpxf6N1Hj9BaJc9gkjRVchw8R4ifu3Ds3Id0xHnBEt+0Q5C6cYzzBWjQMSRQtbIoTxloTS6CCiMhig8hZ2xUUGlmHGhTHZHSXjdTWNZ5UjrEqEP5hD2xIdxqisZXJGEWjbsBadkjIekjCemIh1vVaqHWpv62bvRhsL5fcpKosEPZsiYk8QZoXx+IRAs28KFHXSxIwNmDqzNoards0aKGzyOxovIGmEKUApRLGC0hp/QR5vbwliQ5gRkUAPRsRZSeWQQaoHDSK0kaoiDMkOh3RvU1hhcp8c2+gqTvTAhu0mlCglWml0Vw4xtJM4nnn9N//PH/ivf7kKoG64wBRalyYrvPdln2bgXWdwzzYjiP2sVct40/OP5+RDD+h4fX39+aRGsLMF8zwbLzO3Ok+K/ROp8foLgVKab33rJv7nf+6kOGXch3yXxzOesXhOwwUQu5KaojpAmBPISGBX9QyPi5rEkN3KOFSuJOq2scmgyzpJPBmb4Q1VWgxXDQKz3RmvMnZUtxmzti1UuJMKr6gahb6AF8SEDuBYM91BqK+HVmDG0JaoH+yUI4p5ydRKSX5njOXr5BqVoc/LNtc7HVq3NNucfkHSn12WSgDeZIxViQ3Zo834TkVhb9mD3DlqOkhjqPvxQBe6rwuV88g+ahH2Gx3EmmKJwBRMa8umutCmtBLm3VHBm1SNz6o7D4vnwY69SZhUIyZLSOCwZx4EwNkv+iRlX6NtC+f+LS2tbJqhYkV5rMTwVWtxBw078M7hDdz8wCYueuXpnPv89or2Z64+kp/86PaO90haghe+5Og0ZPgXjJSw8ReAKIp56z98k+995xZKU359kSlN+fzhzs1zHq8xlHRvXOGNRWSGQzLDEUpCtUcSeyaMWK9tivQMpXdVl3lK9tHCkBGEQETKkCA6nF8AVqgMi695XrbA77eIPNHirQFIJWayLaa9FkBuT0x2KCK/IwQVUloosKqmAHnPqRaVeY1F3wqNPJUIYjr2Z9YaGSijbNEm7hVn5D71GQOwqtO8HAVEGmKQPqjh8brhAuOBiuFJ4vFRooJF9iELe6eD7bfeG/N/AVpgVWDvMzOtwshCwLw+42naksrxi7BHJjnt5ScwuLiPM57zEUpKojNG0V8WK7Macw1ktkyZ78+4Ir89pH+dz5e+/1se2ra37TGrDl3EGWce0faZSkpBPufx2r9JNUr/kpEar78AXH75DWzaONTyXv1JeY4HV1OMCzJURpG8HGP7CqeqyIzHZEYjYseE08KcSPTyZtZ4xV7yVWuzGslw7lCcxrD8Wi/CjOX3zvRO6tenMYu+bvy/mabujUc4xYi9J9oUD3Qo7NTMv1XhjZtQ6fAJFjufJ9l7gmT0cEnkgRwax9o11nJ/auYozMHuUy1Gjk6ud5oBi7LSeHr7glpiUGtTOxZrpBZIZYqKR15/JGMvaKXBC8AZqsDEJJYP9iNeR0NrvDABQlJePC0IY0l0Vw7/mQej5vcTLSjwtk//jZmO7dbDqPsky59cQ7N3JmLo3hTwX7+6o+Mh/3TRizj2gF7kI1sRD2xEPLoVRic58MB5fP6r57JwUe8+njzF/og0bLifIwgirv3p3R23C6XnWExNPsQKTfhoxvExZEdCRDWiMl8QWxpbt46nRWLQOp1hH0I/NXaiiI33A4b9qC1BnBEom/r7Zl7G82mnrdg4sUZrmFxhox2B9DW5vcaDWXBLRJiH6qAhmXgTGnfKMBHRMfaGYYLuArrgohwIuwTFZYLKosTjVJrSYsjvZJryB1Tmu3RNdq410IByJPm9msxkRGlQor2m+9fUdNNf0cv4GSvo/c3mljGcrWNER/WZ3Ncs91VjSCTVAYvC9lYNy9KzV5L/3QbsngyVAwf5r0t+wlV3bEZ05xs7SUncnUUUq8iaRykEeC5CWmitwA/QXa38VAGgYO2ajfD3M+c1PjTJe19yCVse3pF4iRoRRujiLrJLulgwL63v+ktHarz2c2zdOkIYzJJjUYBFR3ZeTWBXtjFckBgVIcCxyI6EjB6WpWdrNJN2OAtUxkJZAtmBFg1mkXUmFN54FS0Fcc5CuZLYEYR5YY5v0jgUAFFNFkoYA91sJLWGWCO1pncz9GwxUk0yUvVwneVDZrR2jRBlLYQfYu8cJzhoPtG8RPJJa+wq9D0qyO+EscM0TkWT3x7hjELYb9fPrTF6icVlHl3bZlIXa1cQ9RiyhqxqtCtmZU9WV/aificRroua1wOug4jnFiyeDVpAXPAQQYS9ZxJn9yTXPLIHuXJxvcA8yAGWoHzmKiO3pcAdLpN7dBy7HNU/B5nLQTUkjmMjhdWEaG+17fk/+9avse2RXaYeMLkrNT2Fh25bz+Xv+y4XfLmN1UvxF4PUeO3nmEtARQCiEqGydusC2fR/OUfbKwFgGW3D3vW+YfCJptChYtbFFyEI+zy84fYLmQaiLgeVd7DHjd6f7SuUNKxHGViNoujakKHCK0V1gokGlCcJCi7CEkZPMVTEuVqzRuOF1gzXjJlqsCd97Ac2E3VniA6a3zL/2v5uUbPgDrDHIiSgbIEYj4ny0oQ3EyMWDHpUKxp3PGwxutoWBAMZ4zHFGKHiuXQihWDqOQeRL9nQxPazqgo7igntDp2kk+tUNnhjjQ9ZA36PjTVcbMmpOcMlwv4SOusReRBnLeKcbHy2EoIFOYIFObruHSazp9KYS6DQO4ZRPXnDaJRmRjln5hK0Y/1u/vDLezterooVv/ruzfz9x/+a7v7C7PcmxX6LNOe1n+OAAwZx3U7UN6hp7DkTQV1nD2UWdqqh8ar2RUFMCLRrgW2h/ahRJ0RiINt1M26aQ9jj4vd69dRUM78iKjiE/VkQECfqFYARqS0rvPFW6yqCGKsa0cxqFxjSSWasCsWAzFiAFygTVtTmmmXQubeXAKxtQ6DBP2Z5oszRZs/kvbgrmacUSARu0RAVMrt8Cpsq9DxSxa0Yo1xclqG8wKO6MEt1cQ6VMUZO203K9XNA9WSMp6kxCh7a1OFlt0YdDZfJ1xl1/fyOqHG/c6bGLLNuz4z95bYhFJo4I43hmn4fkv9PHT1InLFa3rd8jbNtFGfjHkQ1QANHHbFsxrzuv+WROa83CiIeuXPjnPul2H+RGq/9HBvW7yEMZ3GdtK57HXY5wh33scd9ZBCjcjbKFTNyUrEjqPbZlBa5lBa5VAZsoqa6Jum5UAlbOhfb1bjFoDWf3xwniHo9qksKhL0eUZdL1ONRWVIgHMxRk1dSbhNVHjNvtMaqahTm/9KPG7JPtiT2bGLPRjmWEfjIQdgDGSH5wif+mpc/51DsoXFkMHuoTUyWKB/Ui3bt2XNpGA9KC1oksKQQOIHAjhp/eFYAVizBs01vsGmGoFO4djqye01bl+Z7A6bvWnZ3Q52/9tvcH02U0wzcW0FLQZSzKC30KC/ycDYM4+xpVfAQgKyEKBeimsfVDsk1VJa3yUspBUGEvW0EEcW87jUn7dP1pUgxHanx2s/xnnf/YDZ5wbbeQ+xZaM8yhcnaiPhpy0g1hTlJdZ5j9ASlySMpT+LPcwm6GyEgK9IEKoRSgKjGyGqMLpZQso0HFus6fVw7FlFvhnAgS9iXQTutXqOeNl/DmGt6XcvvSYHyjGhtbZ5YAp1x8MYUI0d5vPDtJ3Hs8St41wfO4Xs/vgjbnsVDxRjC8ecsmXUfM4nE+2qjrB9bJudVm3KclUmYtb0xtCuYh4BZvFbiGKfSvv5MAIVtIb0PVxPBYOOWKStGVELyOwRRd5biATnKizNEeQtr9yTZu7d1vDx75xjE7TsKNN+DYLCNiK8AQvNg01OqcsSRS2fscuSph8w8TkpENoPoLiByWUQ2w1c+8mP+cMODneeQYr9GmvPaj/GbGx6kUunMajNUeYHl2cS+SbArTNhJWRKn2JSLsQRKCoLe5CvTJlQUdVlYgZFnCudlwBEESuFOmdCUzHvoqBaqqtGshSGMKJBh07zaQeuWHMz0a5GxYT9qMCHMDvPUrk33nUW+Fd3B619wIvMGu1hwwDwOWrWQRx/Z3TFPWDm4N2meWdMn7DDT2vHTPFYNVAaN0HB21BjZKDO7wZSANx7j97WRm0pO072xcwfn+hhTCnu9j3It0KakIcxL4mzTmEGE9+hevId2I9uRdwCVAfeB7ZSe0T/rvIFWIWBIer4pY/jwqOweJ/BDbv75vWx6aCduxuHkM49g1VHLOPFFR3Pn9fejYgW2hSgUCG1F6bAepLbI7g3ZMV7ln8//T3SlytvffTanv/JELnnjl3hwzaPEsSKTczn7LS/gLZ96Q9pQcz9E2hJlFjzdW6J87KM/4aYb18253zOPPYC1Nz8KQGxLtCvRjmzVEMQsdqbQuPOiLQPdICAIgV8Ap6RwKto0gbTM2NMXYq01fi8U9sz+dbQnQ6ymujAN9aaRWoAMjHehHWt24xJGDD3XY8XeLq755j8C8Mvr7uXSS65t7CYb9HyhYarbp3xQN4WdCiueg0QRKqwm5rkGopygtMgYq+7NAe5YTDDYkIASkTIsvTgpAchZKNtQ3atdEPRME07W4EzEdG/2sfZBtco6qoLKSYKHcqAEWBoVg969F3vPFPZ42dR9wYywqGEYqvrnVnz2CoID+qDJKMhSgKiGqJyL9myyD+0lt24Ekc837vvklCG4FPKApjCvh+JExWgixuahYN6SXj75zbfwiTf8P7au24Ho76M036F8aB9Rt4PybGQ1MqzGqQjVXyCzu4Jct62uiC+SOQsg35Pjh9u+gptJNRAfb6SdlFP8WTB7U5AG7rllPatfdiy/+OlagHrd1/SjY6cpvKXaeBdCoFyQkRHP1RY4ZWP0UAqnrIn72hcrR3mBPyCwAkV2rDUEVs/VCNVS0KyhrjpfLxa2ZhrGGRACXIf8JsVwXOZHP7yVv37tybzghUfy+989wk23P0rQZaMyjUJjWVWoRKrJ8rXxFqdfR+05UAPTDJe2jBpJbf/JFS55J8RKWJLOeIBTjlvKiZ2pkKhg4/c62LGFPaIIXGW6SiOQQGZSofftY8Ze4eM9p4SOQFUkw99ZQhTYjD1rMfP+ewPOiEZLjUDWu2PX5g8Q9Tg4E8Y9zt67C3u0ArYkzrm464dwhkr1/cNF3djFAIIYLcqIXA7KZTOQZZnxpWTSrzJ+fIHgIBtvXsg8axJ7eCP/eN6/EocZDnjWKh4ZGmfypAXGa0y+byrvUDx6EFkO6fvdTkafvYReFWGN74KMRG+N6sLRxYky//fUi/naXZ/etxuV4mmB1Hjtx3jBmUdw440Pz76T1qiiz/r7t3PSq47l5l89UCdHTIfQoKOGvmEygFk8LeoLS5iXxE0EDm8kxJtSBIX2SusAQbd5r7zIhLIywwo7iXgqFyr9gtiRZIebjJnENGIkoXyTzCGI27ZOab1sTXavpjoo+OrXb+DoI5Zy2JFLOeD4JVQ2bm413EKgMhJbmRyUFYGKkvorpl2P0hA2bJsGoqwgLMgZhr601CU7FJHdE2BV4gYxJiHRADjFiDALKmvhjQRYnqSU1biTMdmhEKcUJ/fC3IV2dsxEODXy8Crl63oJHsyiKxZSS/zlJhc49LpVZDZNkr9nGHvMRyafs3YldDnMX7GY8d89nFDiJXY5wnp0BETyvVCNL4UAnF2TxvuxbaNWH8VGKBlMHZoQlA7MsuOvBtFOY9Z7dQ+ydylLyrvI7imxdajCxFnLWgxXHVKgcg5TR/Qz8OOHzPh9Dup0D/V8D/VAgPziBNKHTfdv45sX/4Bz/nE1A4v6Zv1upHh6IA0bzoKne9gQ4K9e9nmKxfb1UyZ8ppBlH39xATDNEq1QtTUwfkEa2SBavaJaiEbboKTxvmrHi1DTtcNYIb/HRluiQc5o6tc1tVSgvFYvptZHS1vUmXcL1pSTVvcSZUuswMgOKWHmraRElCpQyHb0vjQYPUZHEGVtnKmQ5x12AB/89Gt4/t99EdVJmUNrlMCI2Opmg5lsV0kfs2TO2hb1c8UZiRYQ5mmZlz0Z0f9I1YTfan+KQhi1+NAUWSsLSoszFHYFhBnDCsyORjO9UylmhHrNPhrrhDLBwwWIReKiNrzb4lKLiYM7d6rODmkW3LERfc9EvUPA9H21Tkgl0XQJL8BxzAOGlGilkD3dVAdctrx5Uftu20nZwor/txEtJXtfvWrmvKZh3rfXYk8G6G4H7UrElDKelxBG5SOKEa6H7Vp84soLOP75R845ZorHjrSTcoo/CQ8PD3HDpo3cs3sXn//i3yQaf02MtdpvpRFln2BRAWKwSyFWKWzLboubPKvmpUY0/daKenNHESrc0YD8Tt/0yLJFYpB0nast4kS5Q2lTCN0ioyTM4l9j4iXGLOx1TcGyZ2FFifZCEOFMBshKhF0KwLZbr3MaBBB0WQQ9NsoGEWhu/fX9vPQF/5J4KR3icMKICfu9Fn6vRZg1Xp9UyU/zvUiEiYUQddkqocGq6Jb737M5aCzezR6vEGjHMrVVMUjfGCunqsmONtQrWj4HpRveTRMmD5bscPqo9Ep0bHJrMtZYsUbGmq4tEYUt0YzjanOUe8cZy7rEPRnD9mznlQuBkG3yobXyCK3RYWi8dBUx+sx8QhVtc6+lKYmYOL6XcKANY7ENwvl5tNBECwSjX1/C6L8vofTKbrSlQUiEbaNVTBREfOCvPsslf/cVyp0e6lI8LZCGDfcj3LFzBx+98XoeHGqI8C7r7mHhEd2M3DGC9pJgltLISgDlkHBRAa3ALodY5RAriIk8G9WbaRk7noMVpwEr1ni7fUQEbjFCKE1l0KUy38FLGvZOX6o0JkfmTGpTOzQLMiNx/SCZsBrtYjBjTDvSRH4Ent3WS/R7LFMIDCA12hPE/XmiDHPmy2peJlIQZ0A5Fu5k3OoBQcfHwsy4pmIb7zQzGhnD1rHYWRt2Y6xbygHaUeLrc1OayBMm92cJxldIqotswrwZILe7fR+x3vUR/oBF2NU68Z7f78Kyu4gXLsa6/f7ZdRKTpqNMl/mqPThpjc66VPKS8kG52btdCygeViC3ZXSWMzZBmTCn84iPc1+F8Kgs5df2EB6Zoedf96Kj2v0x3uxvr7yV3155K2/60Ct44wdesW/nSPGUQup57Se4Y+cO/uYnP+Lh4eGW97dPTvCHQ6qU52uc4TLOUAlnpIxVDhGuRHsWVjXELgXIICbM2US9HjrWEGt04oWpJHTXCQIgVjiTMe5UCLFiarnH2BHZ+sLbacEFcCcUVrVDPZPWSF+T3WPqxayqCQkJv3NRsRWaZpixK4htI9MU5STl+Q5hV+szW1iwsaoRYo4+WzMnb0gpcaY5V6WxqjHueIRVjk0jx2a7LyA7pMiMRGSHOng7zZeesPnsUiNnNBc/Q9tQPMBh9HCXuMvBLgEaBu8J2x5fe937cGtZRc/vtiP65hEPFhBteq21R7vQI/XPVXZl8HZNEednfxiqed7e7lKdQdgRscLbbp6OtAXuLaUkNycIj/CovKirJULQjP/65FX87xW/neuiUjwFkRqv/QQfv/EGYq1R0xb/2quh0/INtYcEyjEEChFqZKiICg4652FFJsSFBqEMOUFojfQTtl8blQx3pEp+WxkZKZQjKS/KMLXSeG9OueGxRBlBtU9S6Zf4PZI4sSNSwcDdZTJD0Yzx3QlF/wOByW8l65jwI+QsyiHmfJqwYFFZ4FJe6OL3Oy0KHWZHgXJNDZvd3ilpuZczTKsQRBnDziPWDcOqjbagOxHhjoV4Y5Hx0JLwojemsKtzGISk5YjfJXGm5igKbkKUk/h9VoM1qgXZvdo03+x0KiAzocnuCk0/NqWx4zzaM6LC2rUMaWIutH34SD60fBZVjph8YTcil/Sp6ZRyjzWZ7VVkoMivG++8n9bk7t/baPCpQUzrg1Z58ez6h1++4NsoNYeBTPGUQxo23A/wyMgw9w+1b+pXQ5wRBAdn8R5piKUKpUGBFcYmj59x66tzfZHTGmFBdqThJWgJYd4yoUStyeypGLacLVEZ85UqL3QaITiNaVw5YBn18eT9WGvCvI0zpfBGQpyqYuCBKlHG5JUQMLHSwe+1qM638cZVonKvyE3N7SWJWM8Q7G0LnTzFxRpnPCTsddrndTDlUTO2SWGMVptTRQXL5O1o4gJqjfIkukhr9+gZ+SLD5IxyNnHOQ/ohblHNavAE7fubWf4+dIAG5t9eRqOZcn2ilQON0J4Q+AcOkFm3tyWE2ZhqLY83zQhYlhFCdhxU1kUHJaZesoz5v9hD4ZYiVlURZy3Kh/Qyemo//oKkPs8SzH94iFWrRxCZYe4YO4qp/j6Tj006HSAF3qZxum/e2jQRiJeZei7xcIR3bZHMujJRt004kMUuaZzRKkSN73Mcxqy98SGOP+OIOe5OiqcSUuO1H2B3sTjnPgI498MvIXdXiV9ddSfVasi8Rb3cNzllFtOCO3NR0oZcIadHtxS4UzEBmCf0hOZdM1wIYXInNeUNz4i9qtq3rZmgAIRdErsiIUlv2FWNHI7x+2zsQFDu0YwdZtG1WeBNMI2q3xky1jiliKhrlq+51jjFGqNC4I2FaEu0HFOTpNKC9nmaOhFF170lhOgs/SQEaKOK71Sa8mUiqVOrdaEWgjhrYSUus/YcAivCm2hvuDWgHEF1wJ72vjbElM53oWUQqUBglC2aUT1kPs6uSazJ6swcnBCoKKpT52vzlZ5nXkig4lM5Ps+iT+7FKqu6cLJViSncM0LhoQmqRy1l5ASXQwc38jf/eRuWYwZ7vt7EL75+KHfeezBx3sEqBWQfGsbdPjmtrAGqL+jCvrpC73eHQDa+L1ZxCjT4q+bhTQFDo3V25I5Hd6fG62mG1HjtB+jPzs3I0sD8QoGzzjuBvz7vudx288N85J3fT/pcSXRCgRbTD5r+Ho0QoFOM0bFhwal2ckwJgh45e3Jea6r9NrLi4JU1SgiEJUyPrIdDujcJqgNGQcMdUwgBsSORYQcvpIkG7hRj/EAZj6+NZwPgjTfihUIIsiMhaiIy3qUnibI2ItJYWqMcYZiTNRFiPzZ1WvW2JjopnhbEbgeVD61xRwOjNdjyvuknpi2Bsi20ZYxRfW4asGyqvYLMeIMqXzuzsmH08EzLvdZo4gyEBUPVb+c1JadOPHGzg1OxiGteTg2OxdTpB5N9eA/uxhFkrf1KLoMa7IUwgjBE2YK4tws5Oo6cClDdOSqHduOu3UVuk0KW1Iz4qwB0EOGtH2KBWsji1QGWY5qFrtu8lFvvOZSp70zQO7rVeE2dJLz+qhe9S9D73aGEgdl0juS07qNDRAcuwl6yELbvhjgm171vrMYUTx2kxms/wBHz5rOyt4/N42MzczIJ8o7D4iDLdT+/l89/6MdGBqrLq2+vtwYxL6i1be8EAQ3KO4nH0LRQu1Oq3osqdk0n5o45G2Haf0htkvT15HpS72NXwZ2g/lSvBSjPauTf2qhcmP3MWPmdVUqLPKPa3rzoacjt9rEC3Xq8MI0t3cmIsMvBihrKF3ZVoySEBQuhFPmdftuFGGU8urBnpiSRVYlxphsuGg8JItZoD6Ok0ZZKbjF6qE1mJMIpG4WNap+kvMg1UlnUTKghbwQ95n5EnsSpznRba0Yw8iRe4sRncQn3ThHP75phwCpHLaZyxEJENcLeMkLO7Z0xovYEO1/cTf9DCnc0prQqojKwknnXdm5jIgAmShBEPPiLVRxyxHbW7l3J7WsPobBmG7nQ1N9p2zb6iM0hSiHAsRC2TeZnU8bTa+OhC8z3J6KMbXVDbzeMjvOs1cd0nFeKpyZS47UfQAjBB59zOm/96dUtT+LNWLLZ5l0/+i5yvIx2bXTuT9d504C2JZp4hnuW2xMR9BjjKBBoMTdbTTTPXif9tZq9v4Zzg4xMLyx7KjQsvNoirzGLWtOiLyNNYVuVKGcR5Sy0AMtXuFNRewq6NvJWQbfTvrYtCZuKpG9ZJxalXVFEeWWU7ZtgT4Wz5p80GA9oFoKGjEWdEFM/JgzQORsiU/wd5TRRDpCCzB6NciWRBu3ElA/RxDmwS5B7WAC2Kfweb3S0FlMlGEzIDtM9ZyGQU9U2hssgLgfkd9tkRjVDx0tQ3Xijo3Pm3QRQzUdEgx7/e82z2Bm5BEs1I+ccwAjg7i7T+/vd5NdNtAgoi4T5SaDIPOLPGloWCqyxCqKvB92dh1gxVKnS1ZefZWYpnmpI2Yb7CV6w8iAuO+vlDORyLe/nbYeFD0F8h6ESC63R2Q4dDusGYB9IDpiFJiwkTRejVhaiO6XI7Ur6SAk9t85irFs6CteKmTsZBjAsujiXdA8OYwhjRGwaMqI0WilUQpePchZCQ2bYJ7fXx5sIZxgugCArEgZiU+6p3fmVwinPTp7QGC9rOqxO4c6m8Zs1HNthendroTVuWeJVxwhOLhI8s0S8LARXgzQFyVpqxk9T7HqzYuJUTfEYzfizNTvPU0w+U6HR6KFheGQTbNhC17Yq9qbdiEoSVq3VaymNvXPCKI3MuGiNiBTZqiK3JaA0IJC+ax4IpNynvJvfY1Oeb7NxyQCVAwuN+kQgmJ9l76sOZOKZ80xhdPIDIGKQlfYPbzOmWTtGSqhUOO9dX2H3yOQ+HJniqYLU89qP8OKDV3HmgQdx89Yt7JyapD+b444fPchNjz5k8hdTPrRpu96CJgNWWwQ6eggCKoM2mb0CGcaGet+E/M4IZVuEBVEPY7U1YlrjTcStfbn2gSUoYo1yGguixoQvtS3rFPig3zP6h0FsiBm6sa8JESZzElBc4hJ223Q/XES5s98nofdN9riFyNE0z7mM3uyDJ3V3NShNdt0IhQ2TBM9YghZTFE/1EN0BFAIjtFiRlAqS8uGJlzxt/OJxESKAzM0lE5KLYxgapa8Kx75jLb/9yrEgHVPgjiBDvn0eUwhkECFiTXaPjzcWUF5m2qf4i/Oz5t0AlCMJF+SIcqIuC9aC5JyjZy4h98AodiU2BggM8/UWn6nD8jgT4x29Ly0hHsga9X6lwQ9wbxvlC1+4lk994g2dJ5fiKYXUeO1nsKXkeStWAhAEEZ//zU+Ia2Gg5Ol3X2DyRcb76bTYBnlJz6YAbMsoF/iRqQtKFmsBeGMRUtkoS5gQVrOQbS0/FUNmdCZ5YS7vRCceVrK7OXeiKh+7En/QhC2lH+MN+/Xj6scD2gI/byG00QVEa6ZW5sjviWYN2yHmNkIiYTI6kyFRziLscdGyce7Zjo0ynf80hYb8bVvIdLmmQHfHFFbSPVoEiq5HeshtCPAXVtEZjSwKVDXH5F/NDO82o3h0yIDfWqgclzWPfLqfN/zozvrteOg3K3j4uwcZZfhkQE3yfz8ynnByfaKpE7TKOZQP6iW3Ybw93R4oHT5gul9nZr9BzoiPJSykLerhQyGl8QrLnb+3tdO6FQscDeWkdEQL1n79d2x63XNZecTMBpkpnnpIw4b7MUolnyhqPH5qW8zIB3VC5EHkSpSkLqTbGMiEjjITsQlfSQm2RZy1jRqCNnU/GnCmYqxyjFTgFLVpOJmEn6TS5HYGdG2u0qAYJKcQs4d/at5JrThVN6uOC0HY1QiNuqOthqsGAYgIvMkIbyqie5tP7yMVckMBtCvGngaVCO/OvpPJ9dnlmOyuCs5E2KDez3JdcdZqP7bWWDtGcfeWyD06Sm7jOFZyDwQgK0Gih+iS3dFNbmMPstKL6vHnfFTVWUH1kNaws44Fk484jK41OdINWxbxm61HMnScR2W+bdRLLFNbVlwoEX5rY0ypwZ5qsAMnTl6UiEA3PuNaS5fywb1MHTMvuZDO8xR+zKIfbaqHVptDhwC5zSWmjh0wDxhN49S6H/hLctg7x9GlMkwUzd/DyCg6CPnRZb+Y/SaleMog9bz2U4xNlLnz/q2InIWqGmUHch5itJSQAehI49YkagyhMuoKWiHiZtnZVoq2eS2wfIV2JSppDlmnok8pVCUkykqsqkgklBSVfsHkSmNktNT0bYhxyokRs6UhbHSAAJQlEFWFEsJ4XDWjIKlrF8pKXO/r1GkcM80Gy9CbiFG2JJzV8xItXl/bPZsKkGv3y6nExnhZ1DUAp4dntRRMLbEo7I4RzaofifyUWtSPv6gfMVnB3jqENVmu7+L3OJA12vbKFka6SgisYgXo6nw9tSln21gNAWsuOwR15ADbe7vQCMK8zfih05YPrbEqEYVtfsvbhW1Vxg9PiB+2ZPTM5bh7ymQ3jGNVIuKCQ/ngPsLBhK6esFg7eV5d949hlaJZPdee+yYoL8sT5yTOqPEmo24Hdywku9M393x43DzMuY7xwIKQG/7rJi78tzeljSufBkiN136Gqh/yhW/ewM9/+4DpTJuR4AlEqHFKMbFjI0s+qivTkWZuVSLTjqMpP6QtRVCwkRFIWxDkLZSXGIzYFCrbpQhrvIJ0HZQlUFnbGE1hRHtlqIy3Yku0JciNQmF3iA51/WFbS9CWQDuANm1Q2oZ+BMipwHiF02j6uikXIyO1Twy3xovEOEcKWYkaAr7T7pMIFUJpwi4XqxI11Xkl22PdaNjZdB5dm0xiwLRu3HedFDhrKSjsjo2XSsPwiWmK7ro7S3jEMvT6XVhDk6icS9SXoVZ60Axne5V9MV7OrmDGe1pDsZxhSnehdXsSSw1TKzMzjFd2l09paZawu9HPLViYh4JHdpdi4kDHCBA33ScZkLTWmXmO/LqJOa9DA7kd01Tjm1Ri6g8K2uS86ggjLn7FZ/n0zz845zlSPLl4zGHDm266iZe97GUsXrwYIQRXX311y3atNR/+8IdZtGgR2WyWM888k0cffbRlnxUrVrQwhYQQfOpTn2rZ59577+U5z3kOmUyGZcuWcemll86Yy5VXXslhhx1GJpPhqKOO4n//938f81z2J8Sx4v2XXMX//uZ+Y7hqEALtCIJuG93toTA05xnq37FClENEoFrJExjigTcREuclfl+iEVhbxCxBlLfw+xxErKDsg2s1KNc6+YmTLsSxQoTahBNDjY1xRASGxiwiTSQk5QGLar/VQk7QmHCdKAbIDmKxdZLEnwIhkNV45j1SGiKNCJQxbBLigoPf45gcUBjXDVvbJX5a2xdk4jUmyhpaCKIuBxm28ipmzY8dvAgsSfmw+YYyPsPaazLryti7/JnXU0Os8R4u47YxXgIIlnQ1BIjbXheISKA8m+KyDFGmsbRoKXBK4I0rZAAojTcaUdgaI0NFZjRCx605T7umYtbmc5TB7GzNpimhchni5fOJDl5MdMACVFe2NRzbXN+YYO1vHuAT536VR+/Zsg9nSfFk4TEbr1KpxDHHHMNll13Wdvull17Kl770JS6//HJuu+028vk8q1evplptfQr6+Mc/zq5du+o/559/fn3b5OQkL3rRizjggAO48847+cxnPsNHP/pRvva1r9X3ueWWW3j961/Peeedx913380555zDOeecw/333/+Y57K/4Na7N3HHfVtR03NUkCyUEHsSujyyC3s4/JB5uHGMEJpYGh09K2q/MNTe80b8xLBN20sItCMJBrKonmz9PTFjDI1TUTgTITKeucDXFmwbyBTBDozwbZC3CAs2YZeFrE4LGU3rP2ZEcWND3MhY+7TQtbteqUFlJEoKk8uLFNZIBYRC5W1T9Fzz1Gq/5yCatLAd222u9QHb54ma/UvHL0MHVbw7N+Ou3zNDY9CzAuZdvssY9hkPLRrhKwb/Y/fM+QgIB/LEA53FbUUEVtl4SyLUTB2YY+ikXsYPyaOERo5O0H39Ogprd5Pb6ZPfFZEZiQnzAiyL7Cj0bopxygHaMkZMRmBPNt2oWNXnHeU65ANrcwa0LYkPWEB0xHKihb3EA13E83uIDllGfPCSet7R5GttsFqZsjdfdTvnn/FJfvKVX9Xfi8KIm35yG+9/5ef4u5f+Kxe+9Svc9Kt7SPv5Pjn4kzopCyG46qqrOOeccwDj6SxevJh3vetdvPvd7wZgYmKCBQsWcMUVV/C6170OMJ7XBRdcwAUXXNB23K9+9av88z//M7t378Z1Tez5/e9/P1dffTUPP2za2r/2ta+lVCpx7bXX1o87+eSTOfbYY7n88sv3eS6z4enWSfn9n7qKm/+wYdZ9BnrzXPz21ezdNcEXvvJr/CY+sT0ZzJlLAPDnZ4lztnnCn75zrHDHo84NHZv2a1dHpQGdk/Tms4xNVpL6IBNOs4oBzoQPYmbTQ+201mUpS1Cd54EAdzTAqnZunwK0DYVpoLogUzeM0ldGFX68Sjwv1zCYybH2hI/lN5h2nRDl7Ho+rx6+Sv5f7bER7Fs9VH2eWiOmKjh/eATtWgRHLCHqz6EdC5W1iaVk6b1rqWwU+Ad4jL1qkMpxhXrvrfztU/T99xBOU6+v2qKgCh6TLzgEnXOJMhD0ts5MRCCTKOGMOWtNZmeFvl9vqI+pbUnx2QfhRFaDoVjbXWiiAkyusJCB2R5lBPZoEXdvFXJZ3NBBlAK6b9406z0JV84nWthnwpG1z8kPsbePICcqxrMuVowmo5T4R+SpHupCGOA9UiHzgI8QEiyLS3/6HjJ5l/ecfSlV1Qh/lw/qZeqoPjzgPy54I0cevuwxfGr7J57INfPPmvPatGkTu3fv5swzz6y/19PTw0knncSaNWtaDManPvUpPvGJT7B8+XLe8IY3cOGFF2InHXDXrFnDc5/73LrhAli9ejWf/vSnGRsbo6+vjzVr1nDRRRe1nH/16tX1MOZjmcv+gnvv2zbnPuVqwG03PcJ//+zuJJ+T/HFrk+eZa9E0dOzYKDm0SyYlTRDbSfM0BtGzFgAvGehi+LatZBJOhH9wP/akjzNuPGYtW40GYNiBrkVt2ZUxZIaqBD0uQZ+LN2oMi552rtmuM66FvpLQknIlMki6FYcxTKtrUxkbGYX1cGm7MbVtCqC1ZcghNVq5tgSRK5HIfSqybUbtOoLDFlI8aZlh2NUGkQK7GBEP5mBzGW+Lz8J/20Gck6guC2syRlaaGKnJZxoN5AkOGsQ/oB8ShRCrakKyWlK/9815uZkTE1QXZ4nyDnYpTHJ3Ajuu6WhOM4RaYBc1bjmisD6itNgiKth4oUd30UGVNJX5Nrg21YMHyawfbvkK1g3uQBfRwt6WuJK1ZQh7yxDTYS/VLP2ij9NXYqKa5dHxeQxFA9jDIYOf3Y27I+R9r/k3tBIQqfqQMtLk142Rf2SMKGNz/g2XctDRS/jaf78XuY/lKCn+NPxZjdfu3SbssGDBgpb3FyxYUN8G8E//9E8cf/zx9Pf3c8stt/CBD3yAXbt28W//9m/1cVauXDljjNq2vr4+du/ePet59nUuzfB9H99vJJsnJ58+FfdbNg1RHK+AN9MraUbOc/jJj+9AdTuIMMbyVUPZYl97McoWIaeZ+CN9eQ1oTzB029YGuSHvQRhjjzeFejt1Ow7jJIckQBtV+czuMpEnCXpdRN42HpgCbZl+W0LNXHhr049yTX8eNbV4FDrrIP2oUZSdzEe5ktiV4Cuk0jMNpYBYCGIHcCRC13qqJbnAmqbkPpQytEAICEOmTlk2ba7JdeQt9gyupN9+AB1q0AKrrIyye9M1a6WJFvdQPn45cY87w7jIWNO9SZmckwa/AP7g3Ky86ooeCg+YJqnh0t72349YGaFfDYWNgmCeQFsCu6To3mbmGRYaXQsqz1hA3OXhrR/GnjJ/syrnEC7th75u8xCVwNo1htPGcAGEOzSlr8cc/rEpFuYmecbAHu4ZXsI98WL2fnQxC9+zHeFlscbLbUPcWoOlBf7Ry3k4ijnjuR9myQE5vvWNd+NlU8bi44knhW3Y7DEdffTRuK7L//k//4dLLrkEz/NmOfLxxSWXXMLHPvaxJ+38fwp+/7tHcHyFn7E676Q1wZ6iEb+NFDLUKGnIHDJUpu/S3M19Tciw7fBJ12A9K9O5o3EVgPC1EQye8lFZh6jHwy5Pm1TS56p+kN0UMkxq0DTGu9OOjcpappBaQNTUUDHOKdyx0HgT0+YS9Dp1kduWU9e0DtvcAGlJdLdLVImwylELRV/VQp9CYE3ERlzYkdhlQ/6QUUyoQfc3ff/3xYglIbFqUjvV9hghiObl8I9fhnfXNnSkk8ZkQFIN7h1pUx7tJ+7KQM5FRIDVeEiQgSI31FAoEYBW4A/ONT9QvXk0wwgg7svQ8uSjNHYpNJ2vEzhlkIFg6iiL/O6Y8jxN1Af4GunXHgQEwbI+gqW9iDA23znXQsTgTUSNBxytsbcMzVLOINh7nceB/1gis8DM4ZjBHUyFHhvHB5l61Tz6/mu043dZAKIaIsfLqL488fwetpY0L37VJ7jmB++nqzvVS3y88Gf1bxcuXAjAnj17Wt7fs2dPfVs7nHTSSURRxObNm+vjtBuj+Ryd9mne/ljn8oEPfICJiYn6z7Ztc4fhniqoVgLsyORlZi7FmPxCpImGKqYOSwrTENExi2qt1Ye2Oi+YGojztpFbgjacDUGUlYT5NjkbpZBBhKyaHxHEM0gFNUQFjyjrEMzPol1pGIzNQ2Ud4i7PfHtrxcm1RVsIo3BvSZRjmdCoENjVpjYcSZG0tgxzMvSkkQzKWIRdNtV5iQJ9GwhAlkNzD5tw2skH8/dvPI2/ff2pvO/9L0vUz2X9Z0aYVCesSAEyMouviymodsb9Bgmllq+ZJTUt1+0gWJCZ3dgpTWX+ANEZK+k+VZA9SOEsldhHdxE+/yDGlh5JePhi7MEevD0hlh8jYuoPAzXDVUtzBvMU46/w0bNpPSU3zJIudJni55bPUmucqaCtjqMzpZl/b4XiS6pMvCagdGZA6ewKU2cVCZY2MSKFMELTnp189rVSiuQho1hFBLPncbWGm9+5vOX1kf27DMEpO7egtAbsiWpjPpbAz/fwvHdexote+WmqlZkMzhR/Ov6sxmvlypUsXLiQ66+/vv7e5OQkt912G6ecckrH49auXYuUkvnz5wNwyimncNNNNxGGjQTyr371Kw499FD6+vrq+zSfp7ZP7Tx/zFw8z6O7u7vl5+mCFSvnoSJFpVcSu9OUHyKFM1rFHi2ZUJhlFlIZKJypCKuahA9jTJLfEvU60doPGI8rGMi0vFeH1ihhNPcq8y2iROBCAyKMDUGhKURXa3dCNHPh0kBlaQ4hrfpiUIOyJSrvgSXRnp3URk3LndQHUhQHLQQCoY3Ku1WNkZERAbaqCruqwbEIcpKgxzFCv516j2kNoUnY60yTuLEQvOTMo3jTa0/hzX9zGk67BpSdkBgpEcXoyNwgocAZD5CVyGxXCUMwas3Z6ShErt2ANTw+9/kSq1OxetjjHsXEwqMoHfAMiv0rCNwuc5+VwprwyYxW6N5YJv/IGFoadZBmua6oRzH6VyFxL6h5YduHpdr9EgoyoyG63/wtuZvHELrmzcVtGae16dpFTe7haRs8qD7Lp3pIG8aw1tgTIVYlQmvzXVTWPsSwNVjrp7jpk6vMuQX0ehWyVojK7msIV9Tr+gQCqSA7AmNdFs95y+f527/54j6Ok2Jf8ZjDhsVikfXr19dfb9q0ibVr19Lf38/y5cu54IIL+OQnP8mqVatYuXIlF198MYsXL64zEtesWcNtt93GGWecQVdXF2vWrOHCCy/kjW98Y90wveENb+BjH/sY5513Hu973/u4//77+eIXv8jnP//5+nnf+c53cvrpp/O5z32Os88+mx/84AfccccddTq9EGLOuexPOO30w6DvOrQniIAoa+jG3t4K+a0ltCWJenPECb1bBjF2NZFWAloUNzwbpUyn31odUpSzUJ7dWGhqj+BJwa47FmAnTLvIk1TnuwQWZIajushuc6Svdl4ZKRTaLGK1J3ABUcaps9HirIM9keQ1Mk6DYDLLuiQAKwZvMkbQaLQpYtrWOlmRRmSMGknHMUONO1QimNfUuFBr5s/v5uRnHVR/65STDuycl2serxpiF03pgSYp0vYxosBZBytUpvi5WSxZCMTLysR5mLytm76bSmaswKibzHq+SJHdGyGnh0knI/z+JD9jiXquy1EOTrmMoLULQfHY2BRBS4gP8pFjNjqcRsBIPMWuzb4xVtmkPc5UlcgJsSK7occ4y5wL90Hpmc0XYX4FR4S4O1xkqfWaM8OmHUrY44Il0HlvTjFgIQRaaaq3VAgmbdxuE6aWsSKMbaNHOUf37rg3hzVaQo6X0J5NuKiXOC+xfI2SknVRmdNf/FF+/bOLcaxZQvsp9hmP2XjdcccdnHHGGfXXtfzVueeeyxVXXMF73/teSqUSb33rWxkfH+e0007juuuuI5MxvYc8z+MHP/gBH/3oR/F9n5UrV3LhhRe25MF6enr45S9/yTve8Q5OOOEEBgcH+fCHP8xb3/rW+j6nnnoq3/ve9/jQhz7EBz/4QVatWsXVV1/NkUceWd9nrrk8nfDLn97Nj7/9e6YmyuS7Mxx63Aq2bRtlaGiKgcECLz77GIrzLIx7Y0KB9pRPYUvJhPu6s0bbEEBrrGqjwWKLp1EjDVgWcdYwwLRjDJ5QTUyz5BBnPMCdiloWQ8tXFLZVqQ7Y9TBRp6drjWn/IZqewLUGu6xR2WQnSxIXXOxiUFeM7xRynA4Z6tlXx9ochOTcV53MN7/ze9oxCmQlxhqrEg5mjI5iYpz6+vJ86dNvwG4iCORyM2n0M8bzI2SpQQ6q7yWFaaqY3A+tG2xEbMswHa/L0H/BXtSUS/WQQbKPDJPZNE7lkL5ZDWa2zYMEmKl6Q/4MDUsBdG1URPk46UlmugNUDlGNmE1GEz6zhLUhgxyy616VQNG1MTTSYJbA73Mov/Rgor4MWiuyEwp3bPaPRiCwyp2tTrAiJPOAV/8ssruq2FVliB01b91ziBf0YO2eaH8u3bgfzt4KO6+fx4pX7KISOPi390NgM3U0dK/d23YOGoj781hFH3v7KKLs4y/MMnliAX9xohOpNHZJkrk14AVnfYrP/783csIhB8xy5Sn2BX9Sndf+jqdCnVe1EvDWV/0/9uwYM4w2rc1TrNUITdV4CsXFDmFXo3C2d+0YdilCOxZxX85INjkCEWucUmJwmsRsp6NG7W505wXlCsOokwLpx2T3zF7wraRAdArD0cTonhY+jF1J1NXE1tIaa9IH25AviFVLkr8TSvMsvMrc0fE3vuYkDjpqCRd/6n9aTVccQ43MYpmESsa1Wbywl9ec80xedMYROM7MJ+mvfPVXXPVft5oX0++t1lgTlRbtQy3NQ4K2LRM6nO7FaEApVNZBC0Flvk32hBKTQwXcG3eRWT/CxMuPIi5M676cLOyZPVUKezqwcbSue6PtPqnYs4izJvyqpWb32zrkcEKwRiW9v7bwRkTdMNQKgotLbfw+u37O3nVl3MnO5RlaaIJFsPdN7TaCtVNSuNEo60st6NpsZDkqi3MN6TJA+CHZ3z7c2rS0dt3QolGZO2MRz7r0fu67+0DuX3sQaI2sagobykg/RE5WcXdMmAcuIO7KIBXIaqPBqNZG1LmyMEPpkC6KR/ajPIlVVsy7w0cgKZw+j8+86WUcvryVDf10x9O2zivFnx/veNPX2DVShELiLbYJR9X+BmVowkF2JSazx8cpmcXK0LFrFEDZmvyf5UldAEQabZtzCsAKNDKIQWjsybk7AgulO+eQaCWeNcPkyGoMAWOo454MVimkVic2V8hJA5V5Lt4ehfbbL5Iao6Tx5n94HkEQkcu6lJsT7JbVor6waEEP37/8H5CzXBPAP779hYyOFbnxp/clF9rwevGjupejLUmc9yDpOVa7Jy0QjbskKwFxxsEbjRnb3Iv0BeHhS6kcuxT30SHs/i5DdKnVZpVjrEqMW5xF43EO51QGMSpj2JpCCdwdkHtQklsnEDGE/VA8TlE+TNP/C4k90Tzvxmdc2B6Zh5K8MSyVeS7eZOeHH6EFpWPaP1trwBqt0PNTkxQLDlkMlvF01LRyEe05hF02MpMzXq0fIkemTChRNwyXtgVR0WZ4fS8P3rvSSKKNm67V0bya2n4P1WcswNk+RtTr0HX/CGIy+b5ojSKGwPRDy2wok9kwysDPt1A+rIvhsw+gOBgweMMeojs3csHnbmPJWYfxre+8a5a7n6IT0mq6pzB+86v72bpzrKVmpbEIAkGEKAXmpxJiF2O88YiejVXcyabcTS00omgYhNpYczjeonau5ve0xqrMot/XfOzMw1ugO+wgALvU1ME3gbKbFiW789dXJ0y9rm0+5S4z4PQgg+lHBZf862sQQuB5Dv/wpufMMlt4+9+dPqfhquFDH3wF1/72g6gum1goIqkJnCaPxJLEPVlD9U/uZf2eCaMSoqxEq1CY3COxNvJXkabv4SoyDPG7BZmJGC/Xh1e1yW/2yW+vkt1eQTmKoMeaU7Zqts9IaJNTAyBSzPuhRf4+gfQFIhI4Q9D3C8nglRbOuKyHDlvGSH5nhxren99v43e3l3rSQuMv1pQO7zAnAV3Xj9Rfuxv3NIgjLawWw7KNjzuE6PBlRAcvIjzyAPyTDyUeaBUqDhYVwII7vnUkKhZ440mOtHbC5EdLQbC8n+qggz0Z1K9NiaSjdxAYj70JuYenWPLVBwkGJcI3RBypNTt/9hCre89rf5EpZkVqvJ6iUErzxc/83Lxo6x3perGwALTS+P02+Z3mKVDIBuNQBrEhB4yX8fYUsccrRgj1j40Yq0YCfK4RtOi8aNaOFR3yVzJU2MWwsXBq4+3JYgAa0/qkDTOyZpQE4E7GdG+qEuQwIdUmHHXEUi7/yt/xrCayxSvPOo5/esvzySZswtqt7y5kuPiis3neqYfOccWt8Dyb//nJhWSW9RL3eFiuTe2OxLkkLCpMm5ja/GPXQmUM/Vt7NipjE7tWXXVehA0PrXt7xMADVaQfo0SMDCKcUOGNR+RGIno2BFiReXD5k/IDKsYeKRmPWIsWA2W6SgvcPY2O2e0gAHeqqU+aEIyvyuD3tdYNaqB8mGDvX9M2NqQ1iNGY3O3FpunF6MhEAqxSo3+YiI0aRr3wu/bbtQmPWk7cXzD33ZGQ66NazeLnbKzAqLR0ytUCeFOi3i9MK2UiEkHY5ggDWVEM/GZ7XYycmjC5lKzuPY+h7e0LqVO0R5rzmgVPZs5r7Z2bec8/fWfO/UQlBA3lxRmCXscYtFjjTkRkdpexyqFhXU0L4QjMIlljgbVD7YsRZ2SL6oMIEoJFpBDxLDkLzKLQ259nYqTUVspHKD2z2WVtu8R4JJGu6yjWWF8a0K5lvK+kFqnZc2mHsUNzWF0OXzn/Rey0bmaTuhE/LtPvLuH4/rM5qvdMLGFWy0o14JY/bGR8osz8wS5OPuHAtrmtfcW2HaO8+6P/zd4to9ijVWQ1NALGtc8litGxRmfsRqi05YaYa5STVXTGIuzJYCdai7GlO2oqahIjn7GxKx2YlEnOa9bPUceGvJNxOoaalUUSIp7dMx0+wmuEkpUmMxLSvclHedIYaVsS52HsZT6qW9enWNfSHY5Z9KHNWBMJW1YKgmNWonsLOGNV0FBdYkgzMphlNlojilXkhm2ECwbJyAwq5zB0TAanKowc1mwXojW9v34UGUNka2QpMF7XHPAP6SazveGB1pdgpZh3zBK+c9Mn5hzjqYoncs1MPa+nKHbuGNun/bQlmDoojz/fQzsi0c4TVOc5TBzWVQ+tNS8p9d+1QuFZnl+0NHVWzZ5NHQmjq23YJznRilULuPLai3jxy4/Dcaz6GJmswxvPew6HPWMxsl1hdG0BTzwGoWmhKwuMR2mVTV2PjJVJyHe6DiA7FJAdGOf6+EPcH/wPxWiUUFfZ42/k57u+xI+2fphImSfnbMblBc85jFe99Hiec/KqP8lwASxb0s93L38LF1ywGmxpGIvNeRkpTfiwneGCethQuxZR1hSKRxmbMGejpWwJp7YcBsjEU671E21+XtUYQoWepSu0UMrUttmz3wMx4wvSCg3EjmjNgUqBN2ZYkHY5hkgR5gRaSfp/5NH9vw6MSOy9Ec46n3n/up0l52+oGy6A6KBF6D4jNBz2Z4gLDs54MCe9HSHQXVnkosVk9pZgwza0XzWF+/vySC8aHqhyJbMLejZQPLSV7VzzxMor8ux6aCcvet4/79M4f+lICRtPUXR17Rudv7LAIypMa5iY/HaKibLALHJMuhKg817re8lv8yQNxcU2MgRvIkYoTSwEViyIPYmILbyxJIw3bfx5y/r4+rdNecP/fdeLueB9Z80QLX3ZOc/kfe/4T7ZuHDKhzpqyRD0ft4+BgTbFytOv1SnGnHLWWmJCdMtCY86xpXQPvx/+PqfP/9t9O+djhG1J/uqs4zhwST8Xvfk/WjfKNn24mmanbBNaFBkH7TmIppCWdoyaiA5irOntYpLjRaRQOQf16A5Edxfkk1q1MEKPjiMGekHIts8xfq8kMzFL/LcGRVKsNpNUVENlsLk5m0YGmsxohHIFEyszBH0NtqSINPmdiqVfi2D3GMKy0CIPvRI9XjTfVUsSLxlofRCwJCLWyKpKykNmn7jqySIsgdgcYN2zkfzgIcSrMgjf7nisBrSliR2BFWiTj9w320V1YYbYKZoidCFAmsjG6OkLqC7O0LV2nFPe+Cl+/5/v2+f86l8iUuP1FMUzTzqITMahWu0QQ09yJH6/23GhsIvRrAWaAtO/a3SZh1PVuFMRIobYNSKzTimmPM+EiZQjqA5KiBTelEIbBgTatal4EquqkuaSptVJlLX4xKf/mg++/T+5+/aN9R5ji5f387Z3v4STnnMIAP2DBb763f/DLb99mBt/9QClok9XV4apyQp33bW5hWn3p8JyY/JdfsfRNJq7Rq/ltHmvxxJOh73+dBx1zAEcevRS1q3bPUPaqmURBsJuizBvNbyVOFEGqTSuokar166FjlWj2LsZOpEzHOzGumdDvfu0iEyrGD00jj5sBTg2xEYbMuhzUBa440keVdPog9UGQmt0TF2Fvvk6AMKCoNov68ZNBpr+B8vEjmD0qDzKmXb9FpQWe7iTCnv5ooSEY6IBBCFs2G5yVQmhScQa6TeuXcRAB490xgSzHvoZB8C6rfSVtpM7y2b7dw9OaPTtx/D7BHvPWsLCa7Zjl2IjUaWiWT0+bQm6fzcMo1NE2US+LJNHOA4q54AtmTqhj/jRKZ73okv4zjf/geUHzJv7Gv4Ckea8ZsETnfO649YN/PgHt3Lv3VsQCOYt7GH79tGO+8dSMXVw5yaBua0lMkPVWdUFAEaP7jVhLIw2YmY4wPI11V5BcZmHW2oskN5YBzo3QMJCtPyYyJV0T0UEfvvaovM/+FJe+poTO85JKc3fvvJLDO2eQPuznLN2aku01L7N2A7kjy1x0vkPMFc86a0HfY0Bb+ms+zwWbNo0xDX/cxcPPbQT17E49dmH8JKzjuZNr/oSlUpyf0Qj+Y8wIbzqgIPyTAhLhsqE9xxzfdLX2EUjr4QyDESSaJddafW+NBBnLWLPwinFMDqJ3LgdoobCiijkUQt60T0Fyos9/PmZhsHUGhFqujaUcQLRObQJBDmJ3eZ5S0njdcWuKXbPjEZkRkNQMHFwluqgPXNMbRiVdqVNPq4mwLx9F+HRK0AZow6N74kSEGdNcXWn7yuKujIMWkMU03/RFqyFkuK6HoZuWJiEI1pD5FEWSksAKXBGfXruGsWaiqAyRWbTTAV6kiG0hKmjC4y9eCH+CkO/lxXo2iQobBD43YKJgwTKEyz6xQRe6PHNb/8DK54mBuyJXDNT4zULnsgP4vtX3My3/v03SCnqXopI/i8sWad+1xEqlCOYPLSz8XLGAro3THXcbvJSgqg3Q+xJpK+Q0wXcJVTmu4Q9poGiW5rj65Lo/3ljvulm3AFCCK655Z9xM509nLvv2MQH3/ldVBijw/bNG2vn1EKAK03ocdoUa/m3k9+lyR/+h2khw5l428HfpM9dNOs++4r/vvJ2vvqV6xsh0QSZ7P9n78/jdTuqOn/8XVV7eoYz3zn3ZiQhwYQwT2IjyigNRoVWpBUVFbUdUBxaWhF/+nVgEMUBRG1xABtpBRFQAVERGUIgIZCQkJDh5s73nvkZ9lRVvz9q7/3sZzw3dtK5tHfldXLueZ69a9euXbtWrbU+67N8Xve6b+OHf+BtKINTWKWHSwiypiSdV4SrKcH6IH5jfEGyEpIt+vhbGqlxQI4ij03gYloyd6kM5RXThQBhwe8VKNNMo5Mesp/iWR8hXR2xjasXSPdE466/YlAXvtjFy8XArVv7rT3IPYGnp1eCVr1sKDnbSMGpx7cn5gIGGzmLd80AQFiL7nXJ9y+iMgfsqc+PbN7DYlH5yHyp3Y+KzdjmbuE7ThI+ogdA/74m659dIT7i2OGNB8mi+6mDTjCWve+4g+RQC5n2mb9h021GCleqMJAueWw9boHVF17gzqk2B+5XdAqWr3dejzPXCFpf3GTXvRFCCT78kZ+dPg7nkJwHbPwHky9+4Qh//Pv/BFApLgBbvORWG6574ePZs2ceMo1Nc4fMCtVEnr5SskUfHUwvbigAG3pII/C7xkGqGfkx0DqREqymNI/FhCf7hKdivO1s8rWFcAH+GYoL3AL7Z7//zzOPefTjLuENb3kp1zzuYvAlyBpopFTmxc9ll+3ml37jxfiBx2g9LKUk3/8zz+Xl3/DCHRXXvL+HRf+BYT244Ya7efPvOWJoO4Ko7PczfvyV7+Av/vePoj3QoSxyupzVlUeS5n09gtVh4IHILI0TMeGpuFYss0jMrQ5yjPamGIe85TnXWtEFqwTpSoRZmccTIRRxSN2QpLvDqo0hKf7evrSBSLWD6xvrAD/akqOxSmJDhfGnO3pHgSU6klOT2KN1PbWdsk+y1cLvZGOFVHUkySNBsqhI5uXAqq06MllxgSVfG8TlGod6HPjG+7j45bdz0fd+iX3/5R68S3tDfQ7v67D/z75E40iXxU+cYvsJK9z7M5ex8TUrdK6dZ/Opyxx+xSUY3zrFBSOUbO4n3gO9Q6ASaJ2AdE/kNiDacu8952H0o3I+5nUOyN/870+jlETryQurpyTdrT5ve9cP84IX/Ra9dMBsEWzkpMsTXC6FdA+1mLtre5BaQw2Q4Sts4M1EGwoAbWgfTwb0NzhmdX87I9kVYoJhFFp9BzxLbv3c4R2PecQ1h3j9772U1dPbbGx0WV6ZY2m5RZ5pTty3ipSSvYeWUUXc48/++kf5+7+9kc995l4slmsfczHPfcGjWVppY61lX/QwTsZ3TVViT1p5IUI8MHu6d73zU4MBHxEBZInmjX/6j7z3Az/J87/5txzVR5FUq7q5SwSfcB5AuJahI4VlsPgbz5ExV14uT5JHRUkWYyHLITPYUCITjb/aG2qzc9kcM0UUZXS8IneqsOysEsQHG1XMLjeW6HRWARgEVNaZVc4cKeeSMIxbeeXlZsD3q2OsU+hk2s3DYizSOUXedPE1HUl0KBA5rs9KEK3lU9zpAtka33hJz4JnaYcxD3/8YeKOx+ovgndHjL9Z85PuWWb/xyTHnxVw6tsLC8uCfyJm62t2uw3fjLJDnUss7cPQPGXRuwxsdRGLc/zJn/wrr/6Fb95hNP5jyXnldQ7IrZ8/MlVxAWhtuPULR/ipn/9LelleWTfh6T5hbrGySbYUVIuAyAxB1zgQRWrJF5rIOEXkprLmdOgX0GwxU3lhLLIq9cDQb2shPJPQ39cYio94nbOrX+QHZz/9VnbPsbJ7sLh6vuLgpXvGjltaafPi7/oaXvxd40wZQgheeOjVvOPeV7GWHkHgkmoFEovhccvfyGOWnnfWfZol1loHONnBy/qp679M+BM+e/ctcPLYhnN/+oJgYzZnpAUax2PSXVGVDmEiD6MtXuxyspBOTQQbiauWXRhfMjXVz7C1chbpAMLVbAu2B27ceMUfBptIQbzbI1jXjhgZp6REptGhYOOaBku3x45qTEOwoUkX1ZgC05HEzuA+rJPquvvSmFBhhCBviGGlKATWpwIaxcseXi8db9sYgiv70+/fgrKaa/ce51PP3I+5oaa45lvoC5bII8mef8lZ/nRG5woPHUBwwtC9IJqpuBCQF1EAmcHijZv4JzqYjR7/8g8pN33z43jUNRdOP/8/mJxXXueAnE0OUawsn77jCEQCmVmiM8WCBMzd0yNbS4lXAqQV+N2ashFgQ4kOI0SiSZYV1kJz7exwvUIbx1gxaWeMWx+CVbeIAu7lHhTcnblzfs43Peas+vBAyNpal+946e/RyXMkF/O0b3gEj/jGdWLdZSU4yKOXn8uBxv1jz9hJzFmEk7NMc9fJNV73/72I7/yeP6gGTmY7U29ZC/564sa+TEGQkDcUXs+hCE3gXGNWDdhYKhnpn5hQW21MrEUkg7pi6bJPNu/8hGWMU4cSpCRdEqiei6PK3KC0Zu0RTawHpx7XJlrNaZ7IaJ7ISBeKEty1edZf8Wie2qG0tx3eWGEsujXdFekOFlgPdCRcPbeaZErTvWueuUdsTnZmCFj7U3jf86/BPg7aL14n+osO6VLI5nMuItkTVvfgbWsWvtSncSYHBL29YjjWNan5MrapLAt3OAYREaf4d53ix//bn/DN3/5kfvjlXz/xffyPJudjXueAPOmpV0zN59AKensC7kvdbjCPBP0Fi0wGrg0BBFs57SMxQXeEaaK2I7ahonEqp7FmdjIInGTa5Rbt8KKozBKe6oO1BKf7pIse/T3+zMW3PR/xtc++5mx68X8sL/yWN/LcH/49Tu1S9PYGdPaGvP8zmt/64QaP2voRnnfBKx5wxSWEYOXA/I7jrH2BNoZDB1d48pMcTdXZIqgEhfuuDo0vKKSMXySWe3517Jio4Xho697uVPez65hzaXqpi131DzRIFn3UVo61mjSyiDijfbhPeMaVe9FtRdYW5MpRQVmv6KN0ifTrVzZQuWXhzqSy0so4po4E8fygosFoX0a/sMX/tw+d3Z7cJd+XZ1mSOUVyoMWZj+6l8yUHNrAGrC5+G+j+QUb+jgQTg0Wy/cIVtp6xzOlvezjJ7nBo/PK2ZPXRLXr73DOYu4fZuzkDzaPuuQVHN4c9HdbiH1vjr97zGd77/pvO6v7+X5fzyusckOd/y+NQnhpbN7QvyNsBKnH1rbyuobGqaZ80E3dvujEjflU0riPlcmPEDouktcgkm72Y1URaQXhfFzzJ+pVNti5vkM5PtiijRsDvvOMHzqrd/1P5nu96C/e2TcGAwECZC0G67PMDr/lfD9q1n/m8R86kXALwexY/g+992R/wb9d/GV2eoKYzXoy2o+JxnkrjSXQkkDPIk4eKiwLheoas8QIOX8h9Fqy5eGt/T4iWliwybFwV0LkooHcoYP2aJiee2CL3oXEiGeR0SYEOx3tifcH6lSFSG1a+ELNwZ0zraMbc4ZSFO/qcfrxH54AaXvSrEjHD92YlnHhyg2RlxrJmLF5XE2xpjM7pLyviJY/ugZBsoYgdG8npDx/gvr+4hI3PrrD9xQXWPr7C6s8ukbw9RSSW8J+6FV5o6+oLXc270XeyeHc6B13ZloUvC658Y5dL/6jL0mdTRD6ieS20vgwiyWncvT7cFCC3Y9js8o6//OQQsOs/qpyHys+Q/5uwz89cfxev+el3kqYaa1x14XwhdJVzRw+21vEXnukPfZfuauy4c8bWguS5JV1QJMseMjG0jxTJqIDIcsd+XRSinJU/5RJtgSQj3u0494zndtZawr7DOe1mSJrmdNMcXbypzWbApQeXaTUDrrj6IF/9jKsx1nLw0AqNZjDxevdXnvJNvz5YlKbIVVnEn/75Dz0g16uLMYZnftMbYCufyOtoQgmeYM732YidpeKlxvEUaoucwJYxKpZi0+JJhyqswbeztsTracLVCbEdcPOomw4R/RoJq49bxrRq1ksRF/U3MsItQ393gPUFaQPMaGypaBdg+ZYYqyQmUKjYsn1IFXlXE8ZKGFRqCDquL/EuRbIgaN8HzZMG2dcEvbxieZ90PxuXB2w+zFk57cMMsZAA+F1NuKFLuGp5c2QtSdYej7nV70dtJQTHO4SfvxeA7jW76f7YInrTQ9/XmmpRRadTdt3Qce9d2Vzxu3eB5Mg3NbC+A5OsfFrQ+lJMdOcpR/I7QfJOh+zxV/Dnb/9vHLxgefJFH0I5X8/rP6A89gmX8mfv/lF++Wffyc2fOYwNPReDKEEVdREC4wnSotQ51uJ1d4gNjIiVgs7FAXmrqPXVVGx7gvaRYkdYwuC1cawLExuxlZsKHPIrXM9BglIK/7ALZsdY0m7soNvWIGKnGOP1Pree7mA8yfWf+DJv/+1/xDZ8RODxiGsO8otveDHz8437dV91+ez1d+youLCWuzY3/93XmCUba10C36MfFqwXBmfxKvf8kM783eonCKxbuMtFTrqinyqdAVhwzaFbHioxqFijm8761r4gWZRYYYhWp50ssK3A1ReLHcGzNLDr+jW6Bxr0LmmBEKh+ztwdfeIDIb0DrnKxEWCiQTuj7WItG5f67PpCgvEteUvhxRYdTUYWSiuxnkBLSdA1NO+DuS9b5073JPiWvKHwO3rieBgBWxcPSIPjXZbGyQGy1uvpCnovqpFzEnRd9fGsPSX2LATeVkayrFALEWozBiHRn57D+BKmpCoKbVm5cVhx1a/cPGrY9bGM3gUB7S+lLFx/CjUlqb+SlUXk3SdmArz+o8h55XWOyOZGj+/45t8m6adYJZHaonoaKzR50xuqXVWlnhZoPaOgvxDidTVyBq9ccTLgguF5s+ZKA7I5j3i3QGWG8HiGyHEB5ly76sX1tkuDXTJALFqL9QcM9LaEgSUuN03i2hPGkiz75AsNlK711Vi8rQSZam696T5e9PWvRXquerTnKZ7yNQ/nlb9wHdGMxOa6XP/Ju3d2e1qnyB9ISZOM3/v1D/AP772J3q7QxZbUdFCOVpZoS48tctJTaCVQ/cGCbQTYyHP8fVAlmuuGwutqdAEI6B1QJEuSeMWneSJFpjNY4wOvGgMjBUpbmluW5s0drBAuLhdBsqsYdyEwft2WnCBCYBoKu91F718EAeGGIVmcbHmpniFaL1ssjvEsbPbxTm4hc4MNA/L5Jn5SS/cAUAIdSdpHNOmiJFkU5C1Bf68lXC1ispuaWT32O5qsOQHoYS0ys6hUo45ukl+0G+/m+xC9zMH+Z/itmsdSB8+fNkTA0s0ZKvboHIpY+rfpissCBD5y7wqsb7n8uv/gcj7mdY7Id3zzm4j7KVWNn/ILC143R5Q7LWtRelBrSOD+HXT1IBg+QQqXOiXHXTIBmgygAzBKkreCAfBDG0jzImel+NHGKbUa0a617vpWuJiL9ZXj25sL0Yuuuq8wls0rGpi5JlKPXF8WbB9t31HpACa3WAupsfzzR2/nBU//NQ7ffXYJm8947tUzk7jB3aDMLf1eyrvf+Sle/l/fwguf83pe/l/fwnv+8nr6vbOD/ZeSZzk/9b1/zN//9WcwWe6ANbM889YiEA5KPulrRJWAbDyBbQaOyNVS/fg97XKdihIySRt6uwu6KSFYvyqaHOMs+6VNsQERKF3AyYvkWIxFaku6Z2ABB+sZMj6LxVMIssBgQwVSInNonK5RMRUiC8U11MNcE33mHlofv5PgrlN4h88QfOkY3ufuohelGE8466/pYRrOup47otn1+Yw9N2SoviFvCbqHIFmwU2tzVV0F566tgCBungsD4UZGvhgRP/oQ5sAK6eX70fNB1V+R24nP2N+aobnKezeWxskMG3lsXrsy8ZiyZbvoUkXE0jyveMWfc8ON9+wY+7r+Qzfzlp99Bzd8+POzO/IVKOdjXjPk/5b/9r3/+3p+5w3/MPV7i1u4dNND7rBmaEWxAE2IsSi32BkFnUOT63iJ3NJYM2AMrXu2i8V1spjQcxYZIPopJnBJz5WVOCEOkqmcfDnC354BBbcWfzMdSlI1kpr1Bz/xa9/Cs7/6qh1Zt5/6gl8jWZ5egwpr+fpdezlzZIv77j0ztAYJARdevJvXv/mlLCw2J59fk6P3rvJTL/sj1k5vD8bcl6R7W1Ovba2ryTXNHQaQNVwFZS9nzDqri/agv9tj+6BPtigcLVFGVRFg/q6YcKOmPKxzD1slK8TipLZ1KOlc0iRvOgqxxVs6rF3dJN43Y1wLWf7wEcwlBdWWtqjMkjUcQW/edOc2j1kETomXfWtcfzdqtTPWH1t0cvUbLmN+tUC0jvTBAiaAk491sblwzbDrC9OLRFbneIJ0TmE9x7/o9R3ZtADiJc+RIRd5Wr0lWzHgh2ua/t7xOb/4xR7te3bmFrW+4sjXzWGlYOGmNRZuWhsq72OVxK4sQBQO9TVbCFjZt8APf+/T+bqnXlkdn+c5337Fj7N5ersaz3Jif923P4Wf+YMHDyx1nh7qP5i8888/MbQr1hKyhiJrKLQvBpBoPatO7cCy0p6odtoWCktIOPeVJ9HBhMduXT0lv6fJQ0BK+vtbQ+1Q+2185ZSJsYheipXONWbVBMVV+1sqn2CW4iqONX7NhWOtqwbdjUvzjtf/3Lv5Ly96Ex/6+8+TZdOpqL75a652VskY4aH7OzqdcuzudQ7fc2biIffde4bf/NX3zeotAHEv5We+73+yfsYtGKXVqjKDtx4PXROALEfedwb/5sN4xzdmWmdyo4dQhbU15RiLAyhkrQK1Wg6d537SBcWZRzXp7FOuVH3uCopSlA+B6W2rWLsKyrjxAvC3Z9N/uXwwTbBWS7YuYn1+39I+qlm8I2fujnxYcRX3601QXPU+zt3VmwokEoBMoXXS9TFv7OwWFoDKLdFGTvN0RrSa4fUMAuc29fuOnLhy19XcwOEWtI7bAY1XoSz6u72ZisviKoE7pelqqvUuX8ResBuzaxGzPI/Zs4Q9sLtSXOWZQjv3++qJLV7z2r/lg/90CwCf+vsbed7C97B5amtIaYXtnCu//gT//Jcf4+df+Bs7jsdXgpyPeZ0DEvfdrlADpuW50uXlpFeKzAOZawrShKkiKCwrKdyPgrTtINMAXmzxOmYQyyqUg9/RNE9nQ6S8WjkLLd7TwOtqVFGxGSEwWOfG7KVOsQWqKLHBjsmhMne74lkyhGCkvKzAhoFbNKxzA22c7PDaX34v73nXp/n13/x22hNqoP3ET/1nur/Q5+9uvmsIvCFyS+NMgr+lOWHWp/bFGMvHP3obp05ssmffwtTj/unvb+bMya2J3/ndDJlq8naAiRRis4d/013OXQcQZ3DlFGqmU+t4p9bIH335zJhN+ez92A5XohdgfFzsxRZVsWtfWyVcGZsZYoUgOhWTLs3hbzkEpJ4PkCmYSQZ8uWAe7yGTwsVcbGocKINi8XWUVqN4Wv/45uxSPhakH+2YStA4ZegcBN1wbO3B1uRNU9lOZw/opufek54hWZbIWDN/r8aaDK/nKKWsBJuHpAuFK1VA0LH4XUvWEm68DfhbbqM4ymRS3QdwwbNO0nxCj0WxwJfuOUCWObcwzVn1/ATdA56r9mAt1lp+6w8+wpMedxGv/pY3TlToSdfnto/s5z99591c/9c7bDy+QuS88joHZGm5xdZWH9P0xuC9QMGQoDDWIkcmpsl1gVorljZfVjvN/u7hIpU6tCTzxctkLFaA1zO0jo+7VKR27ke5nSHjnHypgcwGL6E11tWCyg0i1Vg0IvLOOsF2lggYDtAIh7yjAClUCqj4+o4vneBl3/lW9hxYYN/+RZ773Gt59GMuqoAjP/+LL+Lngdf96nv510/cQSP0+JbrHs8fvPmjWARWFECUKTlR1sJttxydqbw+/o+3OgU7xYKSmcHrpJhYIG+6C0xtLLd70I2hOZzkSpYjjp9xikkbpquu4b56iSWptyMoikQ6MtpSucy2t4bF38pRvcGiZzyB14XcWqfAhljmweuB17fIdovgTH/YdSqFq9ANoGxB0TXoh8jPYnFVcvw9iTPUVuxSPKRAzgeorsT4kjI0NLoBKJ/W1oUe2ZxTsHkLWFEYaZGhRXX6Q+5aYaBxNKG3zyebV6QtQdBxZWmCztDEJT7QIjrWRaXDxAACy6UvOcIFzz2FkLDfrvKoK+7iCx+4nNPBrqkKr5Tti0KUhsU7XbxzuxPzym/6zR3duB/904v5zz92Jz//LW/gl/7qlTOPPdflvPI6B+RHf+o5vOLH3zE1niVwL5ksFkchBEZrBBJFWcFVFAAHw+ojPZonZGUpDRpyLelIYYXF7xiap/PqGqPXBDCtANHLBlYCuEW+KJIp6sefBb2QsJBF4MWzXGB2KM9FAPjK1eyaQPprrWXt9DZn1jvcfvtxPvKPt/I1/+nh/NzPfyNerXT9T/3sC/ip4vhnP+1XqvgJsjhGuu/QEwhxd1gU4n42VXFZIJ8LsA0fefeJIcVVdsHeeRgefhFE4YCOa6NWziZOIZwcpyyvYSXji7q1hGuW1jGN17eovkQ3fFQvc5ZNNhuFV5eFWzdJlyJUbJy1FkiCnsD0QEduMyS1RaYOiCCNBM/DX4vRrQDdGlluamNa74NphTNRfOV42CgoNjoWtd7H2+wPteOf6XPhX8f0L17ERB7aA6VtGTZ1XdAGjUbFgqw9jDaUVrB0S3dinFECy5/rsnZti3RBEXSH263uSwriRYV/tIecayGs5cBXn+LC/3yc5v4BGKgcikc+7w5uOBay/bHW5OdiLUaBTCX9A4LWMeOAIaHk3k98CSHlbIAQgps/sgs/+Djwla28zse8zgF55GMuwfrTS5dAscbmYLTF5Bpp5US3ijKCfR/LKgg11uJ3NOFaTrA1IB0UCGTfFTM0gXDVk70JrA5SYCMPVRDFWkCkUxReUddo1stjhWX9CjUZ/VbeQ6Ld4l2ylkvhqHzEDn5TC7qI33zsX2/nbX/8r9VXne2Yo/et0u3ErJ7ZxhTovolKSY0/i0sfNiAB3trs84G/+Sxv/+N/5R/edxPdbsKlD9+HVMOds0DuSfLFEBu5hVusbk12IaU5fOEuuOsobPcwEmzu0GpiYQ6vm1VtTpO85ZSwKTMJrGXxSznLt+UEWxaVAUqQt32SXQ3Hd1iUEtnRYrYWaSBcdfMgPDNYeCXgxxD0wUuLRyQF0WrR51aI39H42zkqMcjMIlOL33F/lwqj7EN2cGmmNrWA7GfVIbKX4m06+rTRTQHG0rh3w8X1pJvjajvGW+vjnekRrPaJVlOWv9Bh5bPbQ6wXqu+SmqfGAnPLrs908Lc0/aVBMVHL4B0QW32iz9yDvmAR60kWHrfNld9375DiqosxcOkz7yNZCWvt1drUxuXdHe6DsfR3CUSByBU7kWwXcvjzi1z7rKM7Hneuy3nL6ytIBEWMwoiJu8FyEVIZiF6OryXtIykqG0xoI6G3z6e/IAgSQdauUUoVCsPrF0UOwX0nhUNhdxNs5DslNal/xiC7CWZuQjHDQlQnxUt8zjzSY/H2HL8Wz9ceYDVBX1coEasEJhrEqqwUU69fF2vhPe++ga9+yuW8808/xic+dgfWuPIYeTY1r3Tg/lISdOHqsZbv+bpf48kvfQJXHNrHn//Pf0WnGdLz0NrwO6//O57xDdeSNj106A06YBwhrfVraQkz+i6shdVN6PTIv/oRqMgHpRDzcy6W2U+xjWAMSSoAEwjSBYUAkgX3beuoIToz4hysmN8hW4oIzvSrZzXJpSZwz7VuYVtjCDY0eBlWCbKWcnHVWg5gsJbidTVmPsL6XgGisIV/oKYgepa8YTFB+WxB4ZNcdYDo1mOT3XyhD82GK+/iS9SIxTU0poDVFm8zJl9o4G8mQ3lz9bEJtnIWbuuwcbWLP3rd2a678txwU5OsKNJ5h+yUOYjM0Py3Lzumk2svBd/DhIJ9l69icpBTVl4pYfniTcS8JQ4byFgjE02wkQy5tZvHYzBRkQ4BjUm5jzOo4i78qq/8pf8r/w7+HxFHAzV7UbZQlHyfXqm2VGDzRzR+PCGWZaB1LMOL/bKy+YhrEfKmKxcvigq/pQVkWoG7QFLrkxSDBV+7ZE7RSdDN4thSiRmL7KXIJGf39X06l4SsP1wirMDrQ7ClaR3P8bcNptyJR96QG6eMd7kYyZSbr0m/m/KTP/gnaONKwSRtBb7Cy2azGNgihji0IDZDPvmuz/EJPufIirUp9Tr9+Yj3fPhmaPpDCzhlDKzuHptrQJxMBSNYAWbewfLz/Yvke+fQC00Qrgqx2nbuMpXZQrlD2lZkcworJemCAyhgDK1jM2JHwqHbTKAckrO871Hlas0QbFv7EjPvwAR+x+WHhVsGHQi6+3wsluBEh8amwC4MpxcIbSuFU45P3pYkC7JiikEIMmvJG7vQkUd0x0nUdlKMjYCledi7jFQKEo2xZoikepqoboqei1DdbOa70ziVsRVrTKQma8MJUn+PTOA2EiBJHnUhQSKwoWPJMZFC+GcXFZaeQScKEyn8jWRswyZzi0qdV6Vz0OfiA/NsWeu0Xw1lOEmCRsaFj/nWs7u5c1jOK69zRJb2tNg61pn6vcWh/5ACMTtlBXAceTD5/bMFNdFkBeAWkTyQeLEBLCLOMU3fUVYVsHQrBSbyKxQZANogkwyZOPonG6jKUhKpRjcV25e0yBbdIr/wZU3jZB8hiuMorKxZMuGlrGJXI3llMtPkOAViFI6nccqYgKtenM67BQNA5AZ/MyM82XdgjNIrIwTIojpvptHzE6oPF/82oRogR63F7F/GO7Ux9faEBXNwl0vCbQ1bsLrlo9sB3lqfZMEjn6/BNq1FB86NhLH4W84CnynWYgKJSjUWi2mHWO3APCVyVPYzyI1LhZBg5gb3Wh9HmRrah2NkqpH4MIFQRAAitxidY5WrudXbHw5b/sXvvCXg4gUC7SPjIkE+8IaS4iUgkrMs7ZNZyPWOOVcCR3HW369IFzyMZGZupcUx00wSb7QkmxBsnFlgvzozvT0LyXZA1vfBWMIzfbz+5M2WVdA6krN6dcRt6Qat51/Gwvvu2tF1mPYUP/gjt/LRv5l52Dkv55XXOSCntzvctS9n+SQT0YbVVMw0ek7hn4XyEkxfpHUomebWcye7wn3EkDUV7G1TJZH5CiMLpodRkQLTCKCfogoUYinxroDOJQPEmde3eIlFt0On3HKLNTuEtKwdWnzqr6gdLfJnnSIoM+OSOa9Cak5yMWUtRbLiD734VgnS5cC5VgUuJcBavK2McDXB62uyxUbhWh3vua39FtpxDwovhAO74NiZYZACIBohZv8yZqlZ7N6ZqBDz5QaNezYR93UcKS+WrYe1aR/OmbvbKR7ty+GNxVmKUHKgeIzFlKAIXE7SWJ/K88r/dgigGeEYO2xu6V7anDkP86ZEIiCYTgcmhLMeRTo9NgWQ7IomstpPEpkal0pxKsEGApvYiUrP4iizrD8+ziJ3yefO347LfRGCozfv54pvvBvlGyYW7LZw+J/3E57oobqTiZktkCwr5u7JkVrQWDf09kri5Tma2Oku8dr53HP/uFDPRTkP2DgH5HXv+yjGwNrFAuONv/tWgrWaZHfgwAQzwA6V1HeLdUonbfA6mSPynRU7EoJ0vmABl6UydC5C2wiqY0bPQQgo4jKl5JF0iqs4PlrVNFY1KqVI7BTObeQ7yp9JvbJFkC+bU5hAoAJFFHkIz7m/RvsyOrGlGKCw8pEtm1GQLHvjC2lxPzaQgwVKCPJ5n+4lbdK5ArWXM77btRaRueRekeaoMo4HsH83XHYQ0W5iAHPpAXjcVXD1ZciVJfyORvWmlCYp2k6XI1Rq8DdTdEOxcE+C3y+SclsepuWNRJcmiBDI1Llp8/kA7QtMWYrF2vGTSx7LaWJtBTKY9r0wrl3dkJhoAun00PXE1LI6VZNAPh/NQK66TUh8oEne9s8qlaN5UrPn+i3m74yRqa12guV7VwFLFjy6hyYQR1tLsJ47dLAo4ra+xChBnnp87u2PwFqBqdGjlfXC1m9f4Mg/HHApDVNEAOm8z9w9eQGMcc/d+ND/+ivIWyXSw7p3vP5T0JGFh+8f7dm5KOctr3NArr/jPgDsXMjalYa5u1L8Mq6kDdaTxLsj/L7BS9xiK/PJFoSLi9UCzcUELjfEAsCA19V4PU2yFEzcOVomV5qtAj0zxArh2iySX/t7BwmX/rZB9S3xoqS/Ip1ytC7Bs3E6x+8A2aC/QFUQs3XJAhddtovnP/0avu4JV3DmzDY/+sN/xtpadwimLrAsLzRYP7k9iLvVpeXDZlaNR4nSm2WJjv1tLf2DTeZv33Ljm4P13XFOadXicpMYLBbnHFdd4b4d6qF1DBYys2QLEyiYhMA0/Gph9hOXFpFHyh1fHGM9MVwzaugajqUha3jY0fInxjpWjRpA4OwiNcVdWjNxLI0UqKI/1jsLK8haxwwz7WsKxYBjg2kc746BWZCCzlXLBTkypIs+wcbkuJcFUMJZe1IhqFknYvh35sH2JY0RNIm7N9XXBFuGvKEwoaq5RcEqyck7dvOJ334Mlz7tMHuvOYP0LP1TIUc+eoAjH92PNRLdlqhO7Oi7RvqZLgVEXQXSvedC4zYiSc7CDcfJKw7SaTdpYZRX9CtQziuvh1istWT1pExPsn3FcHZ960hGuFFjjFcSg0WWC0H9YGMQJVjPEwhtK5/9yPuHtRBupMS7hpNj3bmAcIzm9WucDdIPIJsPCdZjsLgy8RVs39C5QJEu1V9qQdaCrB3QOp4zd9s2GIMNiyrA2vCfnv4Ifv51gyDz3Xee5G1v/Wc2jm04ZepJlBTMhT6mk7Jx38ZAYRvjctBqzPxlrhlYR0V1f6Xwj6WLvnODlQEx7WDgg8GcoLiqNsafSf3fXmzQkalicMNisUqg5xvOpSUYUlxQKAjjmCyqRb0cc+MscduaUDJGgG4ohNFD1FG2XPimWksCtKOewleUycfVtcUABLMTq0d5HzM3aQBJhhSWzsMXyZZCohNdVDfHCshWGsR7m44YuLj3/oEmXne7uv7QxkGAiYIipjl+3fqxQS+nfVefZHdQMbeIzBKu5wQdgwnVcAHUkWHaPDHPzX9yJaqboYzFSOUKkJZzQgr0XFSQALhBsEqQNTykrxCJQRSJ3jpyIKbWl7fACvKWh+xOqeFWDJ4Vgh987R/w5p/+vp2fwzkq55XXQyj//OFbeMcf/yu9dg+aBeZ1RKx01XbHllclEN3UrSVKOuuk56DUuum5OI2E6HQyE0aMcWzaulggbfGF8Yrdf4k4lLaqR3VWIgS6GVSuRnA7xLwlnOIqjqkfD9Ddp4hO+kTHeg6Z5rnYzcc+civHj6yxuNLmhk/cya+/5j3kxq3KUlvQGqENnfVBlNyo4traEpyJSXc3XDxHCAh98hC2FjTN0cD6/RAdKWR/sLCpZHhRdAqk4KYcOdeOMIVAafFKdKNIyrY4RSNq42VdjDA5MIe/7eIieTR5TG0g0SVk3xYcl8ZZVbY5rRCVU8Q6lMgaq4Ywdjy2OHxHWGnRoSoY76fjZ73YVTTOm1Nch9YitIuNWuWe4dBRxiK7CbJIZ2gc7tA/2KKzMCWR27oSQ7qpSPY0CM8kyExjjS1ivK4CQh00UqFo60NTtIUQBNsZSnrYM0n1wIVwTI06nOESLd4nLzYOrFQAG62xLuZYO876yqVaAGlT0N8XIGND60RWVSLo7fEQ/YzW509DnLn6sVPGvRSVaj550+07HHVuy3nl9RDJO//s3/ij3/sIQsB8W7L2VQ1GaXIAsia17flIXCczFcwZwEpBMu+7irqF20BOcxuV5+AC1DpShRsGRG4J17XLJytFDPoxTRkOtetJrO8WIL+rSUK3pewvS8K1DC+2GCVIlmo71EJ6+0PCjYHLxikBxXe9+C2YKn7nLAchhbO8jMUW9FU6UORzvluMwC2EsUb1MvRcMBTbCoQ/1c11VlJwMFpwZLdFf9O2Imt5FQs52hJ0soobsJRRxZUu+JX70S2g5QBQI7x0iqFuuRlvivUoBCgwJZFsbujvCQi3LSouFIKxqDh3zPLCVQswRZzPihpCrwQgjFpf5XhmBt1Q+OnojQ1cX/WP20cSNi5vTG4P3AJdLuCei5fJ7cS5ZfVw7ll0qo/q53SuWBh/lkXOXbSa0Qtd0UsbeJhgePmzArKWLDaMBrUDak/mBttNXQpJbU5ZyY6udQBdIXrL+xBYXSRtjyjOtCXp73eK2USS7QtD5u6O0aFAndzkwEePuPGYn0duTubYHJVdW9NRj18Jcl55PQRy/Og6f/TmjwCF627bEJ3JiHf5QwrMCDCBJV4UNM5Yl29TfzFrL4gFsrbnFBdM3DVOEmGtg7Xj453uEvYsuuFhWiO78mK3aIXEYmbWRxK4gLzMnYIJNzTJsofq5czflaHSYT9+b5/H1sVh9dJaaxzzea0940uysNCg2iJzg5COId0KqnpnOlJkiyO7byGwkUJb5dxaNUbwYFOTR67kyERAwg6oTFmANYS2yDTH+h7Jou9iefXzlCCd99G+JFoduHTqG4G8pZziGrle5a4qLF/VN9VcKAlsxVk8awBhoHXauBIzgIxz/I1k6Bivl7tSLovRkK9MAORunGxpwZY3kWtnIXmKTFpU7qxDweC4shxPWerG7xkW7+jTORgO4o64RN/GyYygM1jYrXAsK5NyF8u+BdsZwVpCuhwO5r8odhbSXb91NCGdH1FaQPeAR3e/P4jFGUvjlMfCnf2hDWDpmSifkIozhNaYyK8sJLQde/dkN8Nfjx3BtRDk7QAbeoy+RQJB7kHWAKUlxhPEy96Qy7vckCRLitadm0T3nXCtKDW96vmIWKDx+YTbb7yVhz/6EWd1zrkm55XXQyB/994bkVJgaoUSl+5M6XYM24cCtzAYCK0kPGwxkUe6oAk2hzP+84ZHGOckSz7dgw1kLvDS2mIrnJtITHBZVSKEW2iVQAkF5Ih+Dg1v2FVVHOsQZYWlYyfHInQki8KTznpSmaVxLCXazIcXwkKaJ3KEgc2HRTSPxize2h2057syK8JY5FaCWYgqS0JkxtFk5YY8kqiuJluYkHNV/e0C1f5WQh5Ip48z8HzI24FDaSmGF55piqtw3YkCtCW0QaY5WTtAN6eXk9dND93TeP3hxFo3bpMLhNbHS3UdkMMEpWtPoWKNijV5ewZI2rrnhSoUZu74I0vFNXpVkRmC9b4b51oagLPUKJCqtXgYjmy3tES1EhCA19cuTqrd5svlKlKhYf2eYfFLfXTomDqMkqgE/LSmuIrfRtiZVr8Fmoc7lAU8ZWbIG16VJJw3JH7XEG3klTErgM1LAuLdI2MvBf09PtmcYtdntyvGGQFkcwH+Wlz1Q2YGmQ02AMYT5IuDVJLgdI/gTJ0FxOIX7m0z3yiU2OAeTCDJG5DMBTPnX7rg0V7dGIzHXNv9DkPId0jEDyS6K7n5S3/Jwx/9mpnHnqtyXnk9BHLfvWeGFFcprRM5rRM5xhNsXRJBJCo0e9aS+NtmEHcSYJse/UaLeNnDBKBG56sQZC2PcHNKYljhWjSe+7cJJHQLz9B26qrF1hNIrUWmLnaSLHkE23oIzWaBvCHJ28oFmmvflYpr0qsogMapHCNj5r7sFJdVEj0XulylWh9EorGBLOIB0lX7zV0jJlI7lmTBk2gl8FKDKOMaqUVsJGACx/dndGEluDSBMWUk3IYgXMvd2pK7hGhhHDvJTGvNWtK25xZ1bVyhQVwwfkdXk7WFpVWgGz2B8QUqKZRRrF3V5ZFrW2sxvihAOIUiMhZ/y5RGyfhQQcGZ5wh4J30//qEY/o3FGo23FiMLy9gpaY9suYHKTFXCR/sSoQWeAZG7folC4Qrj8qyktZhWiOynE4FDAkBb2vd2nCdi0SdbHCiAdMHH7w4r62xOEO+ZxtUkyJuS7gUh7cNJFVvUTd8BLaYwe6RLg+Ry1UkdBdfImJUGrdzqo1daVZ6gALTIQc1QXOUYKxBxDfJettFqYLvdKScWh5qCv7O5OvO4c1nuN8zqox/9KM9//vM5cOAAQgje8573DH1vreXVr341+/fvp9Fo8IxnPIM77rij+v6ee+7hZS97GZdccgmNRoPLLruMX/iFXyBN06FjhBBjP5/85CeHrvWud72LK6+8kiiKuOaaa/jABz5wv/ryUEmjGY6RuILzufeXPDYvjjC+GC7xXbBTO3cNVDlIUhJtGMJNW7QxPOF1U7lEY2qoqjJuZEFs9mh9eZPgVLd2gNtNqrUYGetBjkix2EgcEi5Z9omXfdJ5j3TBI94dkM855FW9MrBjuJCYUKFDhQ6KROeiT45wV9A6kTlLSwr0fDRYzEslU4wDmkqh2aJopSlKpuzoKhWC5ECD/v7IFd1MHau6jTxU7txd0ghUTxOd7BOsZwTrOapvHPw9s6iuRvUMecPFR2ykqnQEU3enTbm+VQKxHSPSfJDIfbZuP1soqtyitlNUX6ML3epvpsiScaJ4xtZadCgq9GglUpAt+MQHGlNBFRaQ2lSEzbN66Fy7w/ettlPC073KpVsep+Kc4FSHeCUga3joyLnPRGnR+RLjg8WitK2KcLqabh56semKoU7ob/1CXlfTurdbpWyYUBau+cGx/UNyhzuD7oHQMYK0/IrVRE9ggrHg5nd7kN7gr8ZTW69idv0B6bIRltaXthwCdtacKOf/hNaFUoilxenneq7+ntWW072dqbXOVbnfyqvb7XLttdfyu7/7uxO/f+1rX8ub3vQm3vKWt/CpT32KVqvFs5/9bOLYmcm33XYbxhh+//d/n1tuuYU3vvGNvOUtb+FVr3rVWFsf/vCHOX78ePXz2Mc+tvru4x//OC9+8Yt52ctexo033sh1113Hddddxxe+8IWz7stDJf/p667C1EuMAPGCYvWaFp2LwrHdswXiBUvYKcyu2ncD37udbNmIYpFaCdBhsdvSBtFLkevdakccnuzhbQyPi9t9O+44meihpGavb/C3Hc2Pbijn8pICDASbeYW6s6JQMCXyrwAh2EBhPFlwIxaNSumIZ1shY0H82v2IclCEqHgITejKvJyNWFEsZPsaxPtCF/+RboMkDQ6oEnjge3iZRfV1gRYUSCuqe/GSwnqrPScxId4xfPHC1aYdnFqCY4fIXAL5zHOFQJW0X7lBGlepWRWVoqWxBBsp3maKjA0yMU5xKDF1LPM5n7w92fIQlW9NVLGgSb2zxZgOIRGNJVjtTXTzCRyQKDzVQ2hNeKpHdKZPcLrvKjZbB+8vPQlD5xf3YeaiAadgvS8CrOeqfAsE/nbOwu3b1SYhm/PoXhCSzbsq5bkud4NTRAhMKMgXI+eWLawbGwzXrrOexDYDdGvYYlL96VyK1SWyMslYILdimGvgryc7nIUr9FlnH4njaj6KKETs3gWtJnie+2k2YNeyi40Zg0kU779repmdc12EnVaE6GxOFoJ3v/vdXHfddYDbYR84cIBXvvKV/ORP/iQAm5ub7N27l7e97W1827d928R2Xve61/HmN7+Zu+66C3CW1yWXXMKNN97Iox71qInnfOu3fivdbpf3vW9Qov1JT3oSj3rUo3jLW97y7+5LXba2tlhYWGBzc5P5+fmzHZYdRWvDD3/3H3L3Xacw2hHGbl3qEHYqcRoonfeq5OH+Cvhdw/zd01muLW7XbyI5kWKqtLSah7eRSQ7JMOrNgit/0iwq1CqBadTAH0UTcmS2WJwisBJXATot+BARA6h4rY1KTEETNAFhWV5iEnNG9b1wfURbZC+jvy8iOhWTL4STF+riBoS2JCu13CZr8ToaL5mgLK11cbVi5659gY4kXrdwpdWOl70M1UlJViLSldl98Lqa4HQfoaSD+ReStTzn5prW99wSbOYOnKEnc1eWY2NCd4/9FW8iCKTerurmtI72qzkAFATQ7pnrxWjwmR5JeC/+bXwXz6vucSPB205djt20Ip8CNr5qifk7u0P3YqCC8U+to2YLoud4uFSMHeE/LI/VgWD74QtjzRiZO0DEDJetyC1Ld2RDc0YlBm+97xCHSmIjRy2WN7xBvh3Qum11Jp+ixVVv3r5iHpla5u7pFWNg2Lh2ceBlGbkfgMbJHHmmQ3TbkcF3K8uuzxNcxwA2jqEfQ5YBlmNvvIjPf/8vT+/g/ZQHa82cJA8oPdTdd9/NiRMneMYznlF9trCwwBOf+EQ+8YlPTD1vc3OT5eXlsc9f8IIXsGfPHp761Kfy3ve+d+i7T3ziE0PXAXj2s59dXeff05ckSdja2hr6eTBEKcmv/OZLePhVBwDI25KVz3eZuzeheSKleTxl8fYezWMx2rPkbYHfnb3HELjKx2mriGHBwD2I26k3jnTcQiwEhMNUOQIXW0jbjox0VHGVx4z2QuBym7y+QaUOBWdFobjK2TVhARKmDL5P+K742cGbQ8keki2HeKnFBgrZzd1Odowd3SknofVYf3RTjblby37bQKFbLv1ACNzmYkRxAUXZFseu4FbfCZ0vINtebJBm3MpSfY3XyQbH1n4LbQlKmH1xb5OWW0FhMRXH2GlWV+0ejS/RkYeeC9Ht4qcVoEcThmTBRD+yaggcyMApwozwTIyXWYh8bDPENgPsBDe5sM4C04UrztbaK92Es8T4atj68ScoruIeVWoR9RhVsTFpnMpnxxqtJdyspVIUGyBhi/Ew1qEMi/k1mnytWztTUiW7I3TTIzo1sLakFczftjWgeau9y1gIV3OXg7ncRi/UqlRvbNa6bqsfANIMev1CcVGQYX/lygOqvE6cOAHA3r17hz7fu3dv9d2o3Hnnnfz2b/82L3/5y6vP2u02b3jDG3jXu97F+9//fp761Kdy3XXXDSmwEydOzLzOv6cvv/qrv8rCwkL1c+jQobO57X+XLC23+M23fjdPfc4jaB1Pq4TD+k+4lgOaxvGc6LQ9Ozi0EGRzErkVE5yJCVZjoqMdmvdsDwLMRYxpEnFrsNofnhR1XrRpiItSrEWkOd29it5eBzue5Nqp4m2zmoLhoLwd9EMUC78pwRRCIFON6mcE3ZRwLSY83cPbTAauOAv+VjrOAF7EoGaLO1+loOJx5Qe44P5ihDSWcD0dgFXqGwhtCTeLCsbGWStDGwhr8buacLWoOZW6OJu/lRNs5IMdvJ2BHi3HrrTMdigOWrY1VHOsuB/b9NGjiczF3LG1ApLaE1hPono5fjcftzSEwEZ+dc7QsOVgAkW+GGIidXa8naUoiW6HZMtNsqY/cT6XYpTA387AlHQzgnDDMdcEm1N4JItE6WhND743IIv6eFYb154abGaktoh0sDHJlhszvSUA8Z6IYDXDr8efhKNmW7x1i2A9Q8YWmViCjZzGiRyvhtPIvuoQyf62e9e0htU1TLfn+mYt2rf02jma1MW7Qsc9mjws4JL1muL7CpOHFG149OhRnvOc5/CiF72I7/u+AU3Jrl27+Imf+Inq78c//vEcO3aM173udbzgBS940Przsz/7s0PX3draelAVmLWWT3zk9pnw3/m7isKQgrFk3qG2KCyuYoGIziRDLqkJF68KLpbnl2L8Ak89cnrJ0j7J1YcQkGlE4havdFEhc0uwNZkG6Kx3fMVOt67sLCDTHB24wLjsZwSb6ZglqeIcleTo0EOlhmRXMHBtjY7FLIQggqsfcTe333CQofyh0WZ8hS5cgeFG7kA3BYhBZhaZFRuQQglrH7x6aMO6vsjM4ms72YrYQbTvKvrKIl6k+oZ8bja5rdefkKRd/j0p+VkIt0ha0KEoqKSsI3ueJGWKReAham6+vFnmL2nwJKYdYAq0tyxSCaY9kRyLaSlAQWZR7RC2xslms7bvABRFQdVww2A8C7lxLlhg6Ysxmw8LiVeGOR5V3zJ3b+Zyr0KBLuKqfk8QbGYDL8aIBJsZyXIA0qJbPsneJuHJ4fhftQ8JfaJjMY31CYhg4Spd+9uGYEs75T9BQavj6/inY6xSVcM26XPmKfP0L53jgr84TOP0YDwtIIKA8LDlwOemDPBXgDygymvfvn0AnDx5kv3791efnzx5cix2dezYMZ7+9KfzlKc8hbe+9a07tv3EJz6RD33oQ0PXOnny5NAxJ0+erPpwf/pSShiGhOH/vQDmjZ+5B52Mlz2o4k9S4vcGi7+KHYPBJCUmgKw9KHWyIwdhmfdUv66ShatmcgmI6tRUuwC4X4A0rKvXVVqG4XpKthSQNyThJOUlRIWS2tGCyMePEwBSOnqhOYlfKK5JwAAHtTZ0L4iQBvyNtECOeVXfB6UnxlsR0nD55Uf5kZ94Lx/4q8fz/v/9eERZUHGKBVbCu1XGUBXr8tloH5S1RCf7zuLxPawQ5JFwKLZigZKpQY2AQobGpvZ3b6/H5qUB2YKqzm0d07Tv0y4BWI4rXIuzFFR/1sOeEG/JDV4BgPAz5/rb0XoVAjyFFVk17fKFaGoOovEGJL51ya0hWw6HGemNxfQ0bKVD5UDSxQDTHC5zA7Yq5KkFSGORGhbuSlGxqGqZ+V2D1yvnngUhyVuAkGRzAoMhOO6s8NGCo9JYojMxeajI2x7ZUoQJFMGZvkPvQkF75t6fiYqrHDYrCFfjouduA6vnAnc+ILox/n2rRT9FNSnUT8+zIVa45LfvRMaTK0cLI7j7w4d59t6X8w8nf39qH85VeUDdhpdccgn79u3jH//xH6vPtra2+NSnPsWTn/zk6rOjR4/ytV/7tTz2sY/lj//4j5FnscO86aabhpTQk5/85KHrAHzoQx+qrnO2fXko5eTJzclfFEwTo25EKJI+a3718rWUiSZay2mczGgcS7BKjBpOw1JCqcs/pcAqhQ49hJQz4b0uruIYJWRcFJ+sLRDhaoqMNXlToIscsrHLnwV9TtmJiUcKx/6tikJ9020mt5A3T8Q0TiWEqynRqYT2PT2CtdTFNFYTSDVz7V7RtDM7LZaHXX6M6775EwgBz3vhp9m9b2sQQ5h0X1hX1qZ+TM11aITFtH3wC6u3l9LZH7JxZYt0peEKTkYKHUryeZ9kORiLMY0qlK2Lfc48ukE2PzjQBJLtizxWr/Ed/2HNjVnvdbQ6uWbU4H5Al2V6SgBLLXbkLFyN1zvL+lAFQjTb3UIa57oTqRm480rxZXXfZX9za0j3RuOlVKRAtxTpwRbZfEA25ztG94lx2yIGKMA2FEoXVrAUNM/kNM5omqc1fs8OvXd+zzigTtmeGLwHZHp4LhhHR+ZvpjSP9mge7hKupgihHJq2ETgmDDn7HR2FwZdz2V+L3RpgLerE5lgb/qWW26MLmb9lC9XboQCnzhHK5zkHf4TPfOwri+vwfltenU6HO++8s/r77rvv5qabbmJ5eZkLL7yQV7ziFfzyL/8yl19+OZdccgk///M/z4EDBypEYqm4LrroIl7/+tdz+vTpqq3SWvqTP/kTgiDg0Y9+NAB//dd/zf/8n/+TP/zDP6yO/bEf+zGe9rSn8YY3vIHnPe95/K//9b+44YYbKitOCLFjXx5q+cinbxtzQVlgKK+pJqWtpGJNXiHxXCDaiy22QLrnoWOMGKX9GW6scP14Ch35bvGo1emataAZz7EgTG3awvxt22xdOUfvQMDcvcng5S7dbpKKsbz6vCYVu/bMt3uEwX1af8C56krOOFlQDXUNXjcGz0MCP/3f38n7/vwJ3HLDIdLEw0jJHV/Yz6/d+a3s27vGC7/lX3nK027lb/7yydh+7tjKa+XrKwUVSUzDQyTapQxYp6xdsrPLIdPtiHwuorvPI28rGmdG0INVXptLrg03ssEiVDOas4Zg4+HTWUXSBUl/j6J1NEd1EnoHW6CcFS8zA5O9ukMSLyvSecHKzb2JVYVLC7eKS5bjIcXY3M6WG0iki/GWwBPtYkvGrzH/W+tYIDJHRWaBfDGYDkARAtNQ5LFBlOjKHWLEJlSAswS91GBmbKItEHS0Y2ERorJ8AGQvxczXOBrz6VXMR9vUIVV8a/S7rYMhye4AoS3NexOahZvTWvC2U+xKA291e3woviEEC827ukNzZfIg2Mo1/z++/c288/O/wsJCc4eenxtyv5XXDTfcwNOf/vTq7zJG9NKXvpS3ve1t/PRP/zTdbpfv//7vZ2Njg6c+9an8/d//PVHk4LYf+tCHuPPOO7nzzjs5ePDgUNt11P4v/dIvce+99+J5HldeeSXvfOc7eeELX1h9/5SnPIV3vOMd/NzP/RyvetWruPzyy3nPe97D1VdfXR2zU1/+b8itXzzK61/3PpI45wmPuZgf/NFnEwQecZry+ffdAij3wpYv5A4IKwHFiw9WOZeNFw/2aN3dimzR5aDM3aUJ18ch8QJHcaMPzLl8oVij8rMrpw64fCxPVkS0o2Jx7rLWiRwdCYzQqL515wi3WMkSEahktRCU92CUY+X2+9NTA9xg7LxA1Y91bhc1ksxcuNmE5ld+6EXoXFVjJLQlWE/QYcZJscjvvvkFPPNrP+OQkgVAwcoB4MM9E4Eu+CVtqMjDQbxJZKYqMVOyN6TLPuH6jPiOECAtRhjUqQ3E6Q3oJ04xLLTpPu3gJG/nkPT2KubuSvC6Oc3jMYKagpAMAQ7GLg/EK4psTrB5ecTS7eM5kiVZshzho7QwSIjFWYOyqDUyyVUuM3efyIKzMTeoRJM3FAJZVT6YKtZiI1kUON1BirlTFTuub0KmjIPIrVP2yrG55A3lgDXGIrf6mGbg3uUpqQH1e9WhJF2JyNsSmWkaRxJnTXmS7UMB3UPhoAICsLni0elblj/fxe8678srf+a5/MY334G1dsi1nDe9QcL3Wb4eLmad8e2PezXvv+P1Z3nSQyv/R3le/6/L/0nOwvZ2n2/+L28irb2oFhDa8MLnPoqLv2ofv/uKv8RKgWmFlPkcdsKOdZJkLVcyQ8YOom6BjUt98sWa199Y2vf1aR6Jq9iL8SR523cvGlQvrOrnqG7m6JYaE2o8lVJTGLKb1CDvgzXUCIFp+c5tI4Sr3rw1o74QjpnAImooSDs5X60uhQtMTSFrhdKSxVmYgZo5trKbVEiyUclbHvl8QBSm5KeKcvdKstQM2FzvIeSAESXZFY5fx1q87WEltX3AJ5uXtE7s4NqxFrHZo/HJOwYKQQhslnPmWy6j/7DFHefLwfeuITzPxSnLOI12pMY28ouaYOMxsXResH5lkXhrLPv/tTMWizLWoFLNqFTzoVBgedt3oJWZPXXn9ff4jsy5q2ms5aAhXZpQmHNEZGJQU4AU4xeyRKedMtaBmh7HrElnf1BVCvDXY9p3bA0RZhsBNGbHzS2Qtz3SPY3ibxdj9jYz0jnB9qUhWFegNVrVqMyifUG8otAe7P5sF5UY0pbCP3oKsRkPAaiCH/D5wq7LWPrEKrs+fGr2eEuBiIrcTmOwgcc/3PtbM/s/S75i87zOy0Be8KJhxQXFAq8k7/qHz/HPH7jZfVbUJSI3heU5ffdXF1toC92UpG1Xz8uO7kyloHNRk1NPWSJtB8R7W6S7mwPFBdW1dMPDiGJCTGKJqLsyi3/rVugABtY6t6Mx5KEk3t/EetIp1r6rKmxCNQZgLP/9yMddggqKhdWTlBDlHaXMUQomx+iqz6R04zXDwsBaxww+pR3VzcFY4iQs3IXu1fnab3gkv/qml/DIx11UxUGC9bQGJyusknTclWTC0r21830KbbCLc9irLsE+6uHYa6+Aa6+guSod0/sssdb12Xf5bHkoyRZ8spUG+XID3fTIIzlW5j5ZlGxcUWOMkLVabOU9WEfwO7HblKAbTTqvzs4iKsQrYOO6KemWCmOncSrGOjgTj+f5TTq2dozQsxWexYFIqhXTWnTkD1hr6m2ehT1Qr15exuHyBZ/OhSEYy8JdGcu3pzTOaMJNQ+OMZvn2lLmjOVsXOeUohCI/tA8YAKAA+v/irNvNRy3u7Lv0vApwIjwP7RuefcEP7Nj/c0HOK68HQX7ltX871ZMjAITgM58fZMULY1G9FPKc3r5w5gtgKfjzpINhm1Bhmh552yNam5CvYi3LN21VHIAzF+8iwC3SshRzLU416V6EcEi5tIDIJzmqlxNu5UNWk7tnx23oakRR0Ub94E89l2e/6HGudHmpG0cqP0/rL8ZgQkk+Hzp3IIPFtxoFJYsY1w5ccaKw+CaFU8q+lDSEtYXiPX91A7/9Ox/ixk/fW6zUjmLKP5OgOrmL62nHiTiap1dSSTkev9n3qvoa+7BD0Ky5vH2PxrbH7pvT6Yu1dcz32VLkFFdDTSbvlY7VvXNAsX3I4/QjAzYeHoyhCMfy9sTs51SOneNc3NnqGlyobEBgFaRzChWfBX1Wz9Ul8zaLoOyU/C1gkBCOc1EyIXG8fh+qn9G+p4u/4SD5zaNdFBJCDx0q0sWA/qHWOIBjgkyqAJAri/Vg7mhOuDG82Sl/hxsGL3XPSygBSpFfcQBw1rTF0m+1Wf7wcVp3dV2pmml9UaoqD2SlwEYhXkejPcELHvbjM/t/Lsh55fUgyD/+8xdnLkaCATtAtUFXwu2CfEnaKqbqyKQr/8oj5chWy92bEORN5ZBjevi8xc9vE51JhqvETuzUoD0BiNhZGjtagUJANHgRXeB6fEEbKDCPfKlBvhjxuK+/iuu+7Un80R/988Aq2klxlfemrYu1iHLXGpIshuiGhw1cVVwbejMXpKn3M/XaA9JbEWfYXGMtHLl3zS3idbg04CUulyjcyCaWvQ+23WdZW85WANqiwsbE/gkhCNcN7fsmoP2K5OyFu11cxkoqRv7x23bEuNaT9A54rkzOJDGSrO1h5OQ5Ov0eqBj0z0aGri8EWVvibcbDm6q6WJccrFaTQtGMsKyMoD5VN8erkRiLTKO2au2PIkozV0FBZZbm6ZT5OzrOGi/6JxCOYkwKRJYVLu3J72+2GAxZXtU9h8IlRZ92Oz+jBEaJocRtATTWjfN4FMAq24jY+k+XsvWoNnpR4W/1WfqXU+x797EqljWUMyglBMGAF9G42nhCShASL4Z0o8tnPvrF6Q/oHJDzyutBEL2TywK3WOjCWjCAaQTO5bSRke6OSObU8I7JWrCGtK3Q7Qk0OFIQBj6vesHTCL3Cgso0jZNFgP2sXC61/sEwGnDWqYUiNpFfxe4m3jNF0LtQOi/8licAcGatUy0SYodgdxlzE1iUxfnpKdacQJHPB6RLIelSSFan5jkbRVxzJVkcA4YpLA8XaypcmlZgGu5erTEOVO8VDPOj7ZUumQkxpXDD1ebKQ4carK5Tnl789o9u7tjvuXuzwX2WCLLcsudjW8zfskFwqueIi2coGwFEa1OUvbV4XYtteKRLAf0LGiSL/vg8nNZ2mqM68Y4sMbboSLI4UjBSgd/LCU/3B/OypmRkagmPdKo8LwEDxv5+5ioi5AbZzQiPbOJv1RCw1lnFUlu8jR6yl7k0BuPIkmWSu+KnUD1DIQQENTBO0fnGydiVecG4DdTQPQiSXSHZ0mT+SpkXbB6q2EhKQDoPhfUGsHphcakWNVFGsv6sAxx9wwH8JEZ6BWVVSSQcx+5vrZ0yiwt+wyzDxgmi18eWGz3jXKg/9+Lfm/msHmo5X8/rQRApXNhollgs0vOw0qH2yhiKv+Wq2GbLAdmST3ish4oN6e5oqGjdJHnq113Jf3nOY3nhsx7Ds1/8mySr/UoRqF6GnlXcDobyd4CKfWNy/3Evl7UQKFdU7yxyt8qX/Ju+8TE89jEXA+B5ijS5HwERGCwiicE2CwRbocUqa9YTpLsaBCc6COWssfq5wzfkEq0R0NsbkuwqEkGNJVzLCLZ1xZQvSp9wuUAUKQvGd6VRZGYKSHNNYU5wwwoLc4czOgd9kkWJDgx+16KKmp3JInQOSvYfnT2uQrhd/67ru3QujrC+QMUQncnxEoFINTLV5HM7J+DLEqUmqTYK4BbW1snhuFA+52PTnKjYH03qZamM/J5TriZyNbkmIfvK59Y9EAwInYF6DDRcTxAbKWZX5HISrSsNE3T1UDv5fFA878IzYCxyOyFY77u5kmWFq8x9Vz5SYYvqyKmGCWVP6n1CSQy6sgDKuW2VRC+1wHMmk9DGuWybk122pfixwVsfuM+HjhM4guVpgBeBq/Fmwa6lCCkrT4CVCrBYrQefWQtx4Vr1/cL6zLFGQ+5SaGwyzlhyLsl5y+tBkKc85fIdobIiM6SLAb2DbXqXztPfHZK1HLlrtJrSPNLH387JFwJXXn4HxYW1POcZ1wC4Ks2nYsLNvFoQvO1kpssFS5XwW4qwOMVaO8cCeSRJllwCbbISkqyELqbi7QBlLsSPNd/7vV9b/f3MZ37V4MtR62VSX+t/euOB/GrsC2tHL0YOGLPZrz4eaqP8LM7ZvqxFvDd0gJPUMHdf4tx7crDo2HojoykOogjsj1xIFJZQdQ+lUsgsc3entI5keL1irJOc7Qssa1+lSBcF6e6dlY4VAr0Q0ViH5mkIt50l2r+wRf/iOayvhupqTW0HCLcKZaEtMoNo1TB32FTVhAc3JdArjaF442hbALpGaSTjDLnVH8SFrCOONQLSBcnWJSHZBDqrYFtjijIkvnWJ5c3jPRon+mOKK112RLdjiczzIcne9uAzYyHLXcrH6HvhicnvSu3eXRrD+OdmvjEoQyOFY1Hx5IBtZUq7Mi9qmk3JY3MHTfgK904ufzqGTcHq8xer76xxsUbXhBg6p/q7qLps8xyStAJfYTT6LObMQyXnldeDIK/5Wce/OGmKVp/lrjaXKN1ZUpC3PZKVECtApYboTErzVIpAIOMp5KEA1rKr1eAxhSVTfaxUtZDL3BCc7g779GvteRvxxA6LxLG0izSHTJO1VEE3VDtIQt5SZPP+ZHb2Wj+xlmd9w7U0GgFxnPKn77seViI3MtYOEb5OlAKyXR5T0eSMHkahMIx15UFCDzMXOVRdTUG6NlyMJd7fcPdWWBytE6lrqXThKOFch74DEVgLRjrXYllcs1qwyj4ZW7FHCGuLul3WHS9AB4JswQNfInOXH+fHlsU7BxuJzSsbMy1may15cxD/rB5LZgk2NQqP9IJ5TGu25W0BEwjCbcHcUcPKFxIW7jU01pmYnFyKjqQrnjjhIeiGj9Jm6HnKTONtx6i1LnKti7EZW5dF9PYFzjIZvjl3H1t6wJhR+25UTKQcsnKKAjANH93yy+4VVF9q9oZpkliLNZa86ZMsBcTLgYtPlQ1PEGEd1+RY30uX+dlsLurzjPJ1tngbKf4m7HqfJHn8PO0DsbOujKMXm8bQL4q5bo2BWkFg52I0fPqDN+/Yp4dKzrsNHwTxPI+/eNvLecl3/z7ajs9lmWqkVFjjaGe0Z1yAWkqHrJoPCDdrEyn0CU/3ife3ikrKw8Hyhu/x5td9x9AE3b1nntMnN7FbZYzIEdVGRzbRrQATeVhtkblxhfuUcv7w0oViLSgXq7GZLl58hWlMsK7KHJeGQqcGL5nCvF7kfL34O5/Cq3/vA3zwE7dVi4ZcVDTXiwRV6YoRVgqgvFchKkVgioD2zLIZpdvLgs0M+WII0jFgDC0exXFKWxZv7WCVcOzztrazHnLhiGJnbTC+KFg0bLWwSCPImgqpDb19Ht0LPLQv8HsOWBGeHs7r8jsZKrZYAfHeAONpvK7m0Ae7bF/osXWxRzon8LfHXUa2SK1IFjwXn9MWmWuUlvgdPViUhXBYvxLVOTpu1jrrJBxYkjqcDSQpOoBVEtWL0XMBuS+RWmAUSN9ZP3nDx1vvIYqq1c7J6n7rpke21GDuy33SFX+4tpa1+BsZ7S93MJGPngvJ5wK87cI6kLXnKBxcPC+5DGegavO5EK/r3JgCnLWvJLrhYz2XdiKTfKp7mdwxkwjAy0D0wUroHmzhxTlhxxauwnEXocotoqsxgaxKF1kB8aKg3Zuq96p7BEvekAht8XqOlk30UoIkIW9J8qUFmh9vYMKgoM2KUNvxbK7S8j1Ja/mS1iKV5OPv+yxPeu6jZvXqIZPzyutBkj275/nB73gab//zf2MjTZ1P2oAyUE7RKh6VA4lFN3BKIpKYjnCKxQLGcvUjL+R7f/LZvOfvbuJfbriTJNe0wpBnfvWV/MB3fy2NaBh6+9KXPpXXve4D5PMR3mZ/2KffzZwLSEn3aVaAHvwCDWasQ1dlA0JPASSNybGKSqxFt5Sr61X8Pfw9oA0//TN/wR1q2J9u2j5mM3bFLoVbDES9LpbrGFnbQzfczlpkZudEZiFQsWN4pw77Hr2HUiEpRwhruxo7K1kbQEr8rQGSUOAWojzyMJHg1BMb6KimDBqCeLdHdEqzfEuG1Ba/OzhXpJrWvb3qb5XAykbK0q0pJ5/cYOn2nHDTFjl+xUBJQW+3jylz/BRYFP5ailcQDjuLylkkA8t4wnOspSu4OJOdSOg7PG5UzBo68siXI2Ri8GIz2HwIQb7UdKkU/RSrbcHdGODFlmjNVRv2TqZEp1Pyhktv8NYTVMGZmO1yuV56LsQoid9JHbxdiFqsVUwtYDro7zC1E4AJPUwNIGJx1FEVlL7eXk1xDTcCrSN9Ohc3QWn8zbyqH2Y9OWQNCm0d7J+M7oURvf2uFEw679M+qonWpzDXlO+eklhpyeYdBZxeCOnsbzti7swQ3LXNqcV9JF/Vpn3rJmp758rx1oy4Tgt0atI/d+Ne55XXgyDWWl73//0t//jBL2ABtcMLZXFoKR0OYitGSWR/UBvp1hsP88r/+gd8/Tdcy9/+8X8jjMbzROrypCc/DKHARh45EaqTVoFj2xxHOwncS2Wls25kzdVTrWdTYNaDRlw5DtNQjsm67psrD9GWI8fW4VBr7Nx0ThFtGre4lO6ROMdLNUZJkuVwACSg/L2zw0cYt7DOlNJCK9ucVApkgphAIiuEX3FqP2f7UIAOGB6vYpGNd0u2L1Is3lHb/edmDGk5cPnCvn/rs3mJh4wtXuJqaGULHll7uAqwyB2rSlnxuGxHphqRa/q7Q6QRLu8MW5lBojhO5LaqvJzNC7oHJFYKgo4lWnVM+cPjBn7hJbDWQq5RBU/f0EwRDhyRhx7acwm5wXqKsMOM59JAUBLgKoUlJ9/VGH4eviJfaAzKy9TzF0srYsYGq+6e05HnmOdHRQjySOHFetCetZMVFwNrsnG8X2yY6u+PxnYdYMoW7tWsaTnzyFbxcFyL2TysL/g0j2kW7h4nTBZCoL3hud+5KKK/23M9KDoRzwdYa2lsQbZHE9152hVUnY9ciZXVXsUraUuEk5mcpH3xVRdMHsdzQM4rrwdYjDG84w/+hX/6359GGYNuBlhvSmn3Qqopoy0qcYmtMsnH1mVr4SN/dzPbW31+8be+fabL7MjRNayxeJuxU2BLDUSmkWWJkWnnGjvk3hnqZ8FkbZWcvLjXdm4mkHgFj5+tfS+so1qaJPlCAGs9hBZDY2KlLBZphl4wq0DMIjMvFhtnpExWciLTeB0HhUYUOTVFqZSzEiUB7ZRtoTAssHhXRtiTrF7tY4JxK69zyGPxzgHZ7qx4hwAwMH/YOOvJd/RJk+iMGieTIcU12ka4kdE/0EBkBr9jhobF4uJkuQ86gK2LB+VZ8hb09gjm7zaE21QLeniyW1lswWaKlxh0K2DyEu/uXeUWdbSLFDu7JfOVhktLADAWFWtUN0XEGaKAr1vANAP0XIjMBzXUponXSav7zReimkvaIhNdPQvjK/KGh4qLcj87QIgF4CWD/K6hXlhQW6kr7qksq09sjc3n8t+9A4qgY2ieHq61Z8Vwo0lb0t89Et9zpi4yt8gc9HKT9WdcRr5rsFkUSU7z1lM0bj+DoKAKm8BgIqXgWd/xNTPv+aGU88rrAZQszfmVH38Hn/zIF6ud7FlHgjNNo4iFVIpDiTFyTWMsn/rXL/HFm4/wiGsPcd99q6ytdlnZ1ebgweXquCjy3W7bWGQvQ5VdGa2YOyoFBLi+mFoAKYnOJNXfJlKkS8EAfl6IzEvXhiPkrcZA4EAfQpAtT0fPbV8QMH9fMijLIgRWWBdrm+Dqs8rCJNdhoXwqF2ZJBCyo2lHbGV4dYWmtQ/9DYaWehbVmLFaIgt+OgRWTW/wtw66bM049dpyTz/qCtC3wuoZ4T0D78IQKxCNS8khWrrKRNmVq8PujkMDa+bjEaZm6WJ1uSFeIckR0BNsHGS58WOz0ty6RLH/R4HUMweke/lZa+DidIrJSDlIJpnbEAZBEY/amDtx8Mji3VrCZuQ1YN6nup/wteykyzkh3tcHzmOgWLQAzspMOwD6+m78i1ZWLshSV586FjUV0YrKlRlXja5bMssxUkrN9WTQcux4Va+kcUAPlpVyRUVeUdnBOvDwd3Suse2+yhoTWMEu8DT26jz7gNgPrXUSSIaTE23LWnlQSYww/8sbvZGXf4o73+1DJeeX1AMrbf/cf+dQ/3QbUXqz6jrqA5I5VRLUWNVIjyf3DKQFXyn5wuFKSv/zTf+NUN+b2249Xn1951X5+8AefwdXXHOTSS/bQbob0t0d81sXk14Ekb3musKUtajJ180L51LoGY4moApCxJjrRJ97bcP78GvR76Fol4ik3iAIA4W1n5HOT3Z4i8OjtszSOxhVWwkxDj0GRD1QosJE8KpUYpHYLvr/WJ5cSXeQ6yX6O359utlXJ1LX2JomsM+vXrC9wxp7fs0SrhnjX+EJjFPQORoxzLk3oD6UFWX84wwt0yZ+4k8jUYAIPHYLqDyuBeFGwfZG7jtdxNGF5q04tZsmCjPm7t9153rD1J+wor8SU+0myQQmeWX3NDf6ZHogCSNFLq74OtQdYY/E3+5iojfWKT2vPUMY5wekuQjqmF1Okn4jcVIprtF23JxHky02ypsLvzY4BzdLbArDakizu4JIWgrwt6Oz2XG04ZWmdGnkvBVif6e+FgjwQQ5u1UrwzXVo3HMVf69Xu02I8hUJw1RMu4yU/8wIe8/Sv4lyW81D5B0jifsrfvv0TjPKIyThDJDnemQ7BmQ7+ahf/dAdvzfGOlZBUmLFZlQKSFJE5uLwxho9/7Et86Usnhg770u0n+Ikffzufu+kwSkme+4JHT7RIsjmPZHeEbigXTPYledsj3hORBwMc+ZDiqrvrBNhQYUIPfyur4kVevwbUyE117wN2AnePzaN9ZKIJzyQ0TsREp5OhBOloV4sX/7enY0IP63uYneJsvsQEOLdPagpF7FykFtDKojKDlQK1lTqW+242dZEV4MAiSV6N2ZgU91zlbolicRf1sSviIKfHlYrILNmcD9a5SHU0mzrJwhDPoJxUwmZnHTh8nBBVMnARrqNzgWDurowL/rHLvo/H7P9EzMEPdli8te/cZkIQ7ynciROIjqei9KobcdRaMjc7k9gK58qSqUYWsSphpzOwCIAkRyY5wUaKv5ng9XK8boa/HuPFBjPfQM8XCf+lqzAeVly29jO4L0O0ljpEbvkzQj22k8E5dOBZaPigZ2lsWZprdqxhK+s9nnAJKZwCn6C4Fj58J956b+wckWtMaHjxTz3/nFdccF55PWBy923H6XXHKzQKC95G3yGjys9wbgp/1SkwlejZk14IxzumDSLJsEXisOimzv1S/G2M+3njG/8eay3f90Nfz9xScwh4kTcU6ZyPkaB9ifYdrNkWC2+6K8JSy8spF2Xc+6YjD70QYhqeY7EPFP52jtetATQKhN+Ii961YUEay9w9XcL1DH87J9jImDvcp3m0h+rn/LcXfjVf/9QrkUVwXGrOaqFTiXElN2os69qzBMe2MAKEEKhME6wmVdszh90yiHNY66zHtPjJ7DgjSWEJWRgCt4yxqVtL63CGqwXtJF3wp/anvBtZJoxb66pAl9csJG+oHY04i+PGnNR+vKRY+XzC/FHrCJdD5ciUmwGtE7D3k4P41jQRxjqraprCB5ekLMWAe3DasaYgMy6tPjPbqrOAbQWo1HkqpHYeDZVoB5rUBjJTueJFbhD9DJEPFKIVDHL6lChWSFerrCz7MggJ2EqB2eKLnfqHcNWrZ04+a/E6w0raqmEDXRQVoGdecMImovWZo5Sx5+FDi2O7hn/75KdmNHruyHnl9QCJGXkBrRSkS2GxQ5rs5sCCtzU7B6N+QrVhzhxprjRuQVVxjuw7q8xay32HV7n9tuNIKfmV3/tOooaLuRhfueRYT2BCifXcS2EC6YpGKneRvF3EpNTw9LCRcgqrsjLcj0A4iHtZGTfOB7xyE8QW/ysXgfK+/J6hfSTm7a//MG963d+hiutLwOtOyR0D53bdTJCrHYzQaM+ilYbtLtHRLQg87EITr5PvDK2vN6sEsp85Sp7MVkUGBVSLqvXkiKJ3B9TRjXnBqVuOT+NUTvP48IZFNzx0IAfjU5PBYgki02DAzyXeRlF6pUw4l4JkcXJZl7LdbM4bpAwUFrLQGn+tx8KXtgl7NUu7tnFBCbxU0b43JVydXa9GdhygYiwZ3li81S4y1dhwAMIYdLB2vKXgFxxeoGeu+YEr+zK2aSrvQYpiM1RsRASI2hLoFNfIFQqlWVV9rn9V/sMYsjlF58LmznNLCRpHUlcBfNpmTAiap8zQ3+CsrdKzI7TF354xmS2Mqnq1FTuk4dTLurzJ9731Y2xNqNB8rsl55fUAySUP309QwNfzhkfn8kV003dVj6ecI8Ah+M6CyJeRl0dkw4UNRc39AXDixCYAlz/iAl71+m9DN3x0oEh2hQM0Vn1xwrEr2HLhlXLoRbaAiabnPdliwXnKEy9F9bKzVhCTpLPV5ws334cxgxfYS4xj8R5a4IqdfGKQmbPoghMdwqNbRMc6qF7u+AabIS5yMVAEO424AKySrtBfuWDU76rmJrQ1ZWCFIJ8LqrGzQNYA1dWEq5pdn+2zfGPi4PUjMo0tpPq+KIFRSut0RvOeDuFahuobVE87d1HNdVn3UOUtRbISVN8ZBeFan/BU36EES8j4pGdc5BfN3ZvRvm8ERTph7GQvxTuxhdrso7Zi1GoH//imU2pKDpRXad2WispYV0JGF65mPaCRwpvNhHE2FGqldWR95Z6vFAOLqeZOHbqf3My8V3AbQBuOEDnXL10NjEAZ2PPxjkPK1pV78bt5XBNujZlGRfqIRXUzgs2UuXtjt6kauaDspjRvOYla6w0pSNmbXrS16qe10DH8+NN+ge7WuGvxXJLzgI0HSJqtkOe88HG85y8+Sf/CeZBOMe3kBxfgqFnkFORQOblHFjsxQrnjXnRb5b3Mzzeq7371l/4GpMA0pHNnTeyIcK5HX7iNeaXY3FV2qjJbuh0e8chDfPb4cU5njm4qWs1onM7GXWc7kfha6yqQ1K7p913yq45kAWfHuYQ0WCGJDy3gbSeudIZxAWiaYWXlDTXvCcgnx08qF5CwiMibmZLgFhVcBWohHABGCqpcdAGLd1mwuSszYy163q8MispdJRkiiK36IkBHijySSFO4IXPrXL2BwMvA28gIN2vgEykpazuZUGI8STrn3IBl+1ZC40gXmQwAJ1NplWrPROUCmRps6CGSfPgeyt++JG+HqO0E0YnZvmqO3gULlcXndTRz9yQEXVeA1VmytfW3XHDzgSva6oK82lfYbIqbfVaxUajmuPUKKqvSoinRteWcr+d1CWddl4nY0yTYyskbHsKTWGGKgq61cSnckFa4KtzhluHAh7c4/cQ58pa7lt+1NM5o/P6MCwkKgmiBn8LSbX22Lg7Jmw5PPPep+2jecgos5IsRm8+9qqppZ3ZS7rj32DYkR+44znvf/EFe/DPX7XjOQyXnLa8HUJ70vEeS7moM8jfUjBepJi/70Wci1YRHUe74k/GExUkicEptcbHJtY+6EIB77j5Nt+My7PPmABU4uQGBLcox5C3Pkb0GNTfSWeQ+vfFDH+fIoiHe5RPv9tm4qsXJJ86TNWsusdrCMVWmaH1hHTDE72r8nq7IYgWOqSRfbhJfskz/shXSC+YRExTXoLEpO2Qgmw/wtlJs4O1830V+GMIpBdFLUb3UWcflfQqBjTxs5OOtxcgiXlJ3OZqRjYWVkCyFpPMF63eSIpKMrCFIF316BxukS1P4JIVACInMKBYuiSn6l4dOMYcb+Zji2Vmc/Wl85aza+rlSoFsB+XwEniRbbnDqGfvoXdQeMF8IQd5WrH9Vk96ygqJMSLISVuhX60mnuOobtsIys5Ff5RjakZ8db6J8jmNpBnZ4M1V7ZoBzO096P+ujkhmi9QIEpCT4bqNoAwmBHEJl2mJz46WW5Zt67PlMzL5PxczfZwaKq4D1q67LQ1Q9V09MmGGPix8blm/rsfD0kyx84W6aXzhVzXl/I2bhg7e7OmWAXozI58LZ1qu1JPvBGsv73/rhmff8UMt55fUASj/OyRYHlW7zudlQYAsgBVdee4hf/93v4LIr9g0fYBwya1ICq/YlyZxHMu+jS4604rvv+/6no5RE54Y/+6N/oZzqk3KDJolINHnToQ+T/U3SheDs6mEB2g5ykcrjjS9Yu7btrAvBWCxtYh+gKkx5tiJSg0g0MtGoni5YS9TEUi0it2RNDx0Nu6KML0kXA9C6SGY9y2XdWmQ3ITjRIVjrE6z1iU50CE5sVy7e0s2o50L89Zhg01RcjRZXLbje02Q+wGJp3LNB855NghNdghMd2neu07xrE9nVGH+HQpbWWTqt4ynRmkP5WU/g9Yc3RJXlvpOiLtf/3CCldK66wMMGHibyMO3ALdRSsH2RX/H3jbtcoXtxhA4kVklMoMjnfPfOKIkZgdELcOjcTGMD39WO85SbS57CRsGwC27iYIixeSzS3AFvxIx3w9odyXuldijXqoWRuHD9PnSksE0P40s8LfB6jny4DmDxuhq/Z1yysXYpKH5XI3NJvOhRtwMFAnm3Jrp+bWwu+Ks9Ft97C/N/90WaNx0juWgJsGOo6FJMwyf8nNOgq8fWZtzxQy/nldcDKHv2zLt/lDssT5KuRBMnfWlYZK2A//5Df8biSpvfe/vL+dn/3zfR8iWilyD66Zji0kD3knm6D1sg3RWRroT0DrboHGxiPHjxf30Kz3nuI1lf6/JD3/0HfPRDt1bnymyHqsLWIfVUbBxxb+EqzJdC4n0RdsakL12bc0cnsN9LR/L6X37hOTz2q69AniX1krCWXUtNwnCYd26qSFCaCpQhcM9ANzzMyDUF4Hcd6itZDkmWQ+JdEdm8j9dJCU90XRvxDtBv3G5VxCkiHXdnyUQTnugUVQRwbXkSYRx8v3kqIzjdR2iDbjj3oMWVVbGeoHHvRoVqLO8JQHVSWndvooPhSrtjfatOFKjE0jyVE67lE6012T+LmIge3Iebw4MFWuoirmJdleuK6X6SFJZm3hCEZ+JB7bhSuShZER3nDUXW8rCeSwZPFgPnGg08bOg761g6N7Dr5ITRKF3vdWsud3XOdmRUKfozaZcw5LrPBqw40955K3CuT8/13wGeXNNeUdrF6+mqfll5yfK3zEEKSf+CBr2VgYK37zdTJ4EAgtNdmp8/jt09T3rJrqrB8n221pLtbtPf41eu2rml9uQGzxE5H/N6AOWyS3bTjgI6aVoteOkel90erI6QYwpIl5vodoBY7fPql/0hq0fWybNaaXFVBJWLUzTQvWLRoarqi49wOR29Ay3e874bsZ7gvf/revrb8ZC7z+9osvnZnIj+drEjH3V5eoq8JfC6E6ijai++F1v8jhmrySQE3Lp6hp/+mefxY9/7R2xu9jCZcVZJidzza0jGogzLWj8l3tdyrPfW4vUnxxFNzaIToxe2FhOqIk9o+DwVa+im0Awcy0JnAKawwiVIl3GZSUrM4tgPgnhyTETgXDDeVkK+3KjGy3rKLZyBItrURJsuOL59SNA+4ixAf70/keqpbFfFOf56SrwrIDqdDpSecjv1Ms44CgSJ1jWd/R5WMDQeqp+76r+jRTuL2I8RtQVDMAx3L9tIDTLP6O0aLMxTxTgF1zyZ0DiWk80HLpG9kGw+oLc/GvTHWrzNFJFZRxQzArYT4NJIyo1KPcncAqljMSkJbmVBg3Z/0EVVzHDopoUDmVggTt1GyffGjxeORd+hFwd9LsVLDNjcIXeniHvuxm1yQkV/xaexmiHW7YSOTTg/zUmuuYDkqv0EXzqB7CTYyCe5Yg9qvYe8/V7AsWw866VPO9theUjkvPJ6AEUIwVMfdyl///Hb6h+S7m2RrTTwtgpyXIoKrlIgkgy13uXkme1isRCV+8OaHFDYAmGW7apVUy7hxLX4kbCWLa3587/8lKswG3gFqwcgHd9ZsJGRLk4oG2GtYwPv6KLA47ib0HqSvOVenqFS7LlFGON2woDftWRzw2NjLSSZZt+BRX7nj7+PX3rF27njs/cO5aBZcDE3T1YZUFnLjVOJ6MuVxOu6He7QucX3E9ehCoyiqlyd8tye36NxvAsX7Qffwyx5mLxQRKqoRqvtMLy8FszXgUB1kpnAHLerrimvsp0SDFO01V0R+BuQhRZMDsJgPMcFOEks4K/HpHsbdC4MyZrC1SODogJ0TvNUjtJi/LyuId4TEp1Mhnb33maCbvqudlZ1z2CNQRk7lMtXxQxHCYW1QaazSCcnjJF13IgGNxa24ZPsiZz1D6AtjdOJs0oQEPrYsvpBwaJiA1f0cejdAEemm5nCdWmh2DRUMTVjhys3TxJrsaGPyLS7rmDA8SkE9FMXa7IuqTpbjCCzSGPcJshXg81VUbF7EmRd5WZH976wBW2WL1zeJRn4wY4pNxYQ611YaYMvSB+xv3h5BM1DHfjAPchTKdJTzC23+aYf/YbZDT7Ecl55PcDy6Ksv5IP//EVMOKwArBJkyxEiNUMVVYPj2+PxpGLHaq17OdMLFkBJkgUftCHYyvG3s4r6KG8odMMr+ACFQzVJSx4oRObYJEoUor+Vu3jPvFcRxrqckdx9B2QzXmTrSfK2BGMJTvdd+RC1M5JJCsEjDu4B4B/+8lPc8Vm3w6uUVvEjtSvFUObH6ZEChGWtLZFbx6AhIG0rgq4er/RbFyGGGCqSZcXGFT7+3X0at41Yxd6o1ehon2C4hlTWEOimwtvcOThXlXcpFJ+Ic1ccskAY9ncHDra+mQ2SUS0gJMmyIlh3EHPsgBVd4BRFHrh42ZBIQbLikS14LNyZuLI7ZV9woBeMG5Ny8S8v6/UyMmNI9jbw+oagl0+0OGxxHeeyG2ZXCU7m2Ksqx+JkkYJwfdhV6fIFDXQSgjOG/KI5yA1zh/sFZ2L9PcFt5pLc9aFUJKMLvyxiYYWLMJvzhotr1mN9k5RG6XKUxaZzwvfWU4PYZvmcQ4lhPCG8TFXxkvFk4fsjAqfI8jmFv+UqWk9yXYPzImS7mzRuOkJw+Tr6kbvItgK8hmb+yg2aF3Y59TmP/LaMS6+5kFe9/UdZ2b/07+/c/wU5r7weYHnSEy7DM6D7Gu1LUOXuztU9qi+wIs6RM5J5hXCMz97pDvm+ebCW5qkYmQyzdXh9jdfX5HOBK9GRFEUlS+Rg08Pv5W7HiPOpez1dAChE5ZqqlMhONZGqawt0pIYBGBLyUBKdcS+mkY6R3EbwLU+8hi/edJi/+L2PAA5AYiN/iKpHJ5ljaJBebSESw6smLh6kfdd+tqAIerOhzOB2yv1dAWce5aObCrQlvC1HC01/v4cJBTK1RGc0Xjq8qggh0Bi3WngOWu31tXP1KIVldm6blW6cS7Jk5hqOwV0J+hc2aRzvEGwWJVJqlzZCoIxPvjsaQPYzjezEiDRHNxRpW5SdHHlAAuNZevt85o4MlISLvQiypQCZOqZ2kTvYug0k6VKIbjo29aAzTJ002bJl4EIslLPCbYqmIm6NRfUNwcYEC61oI9zMMUe7roTLqOKq3a8NlCNenjZnReGSLuquBZsZ8b4Ib7s291MNgRr2OFT5V8WPtjBpY1fGxMrcS+u4M02tHpwFspYka6rBmBiXaBytD6oLMOk+R6T0UJSiA4mvFMlXXUB04+GJ6RbWV3hzGUvf69F4ToYQJ0ab5donPYzv+qVv4+GPv2zm9c8VOa+8HmBZWW7z3Gddwwf+4fOIZPaOXMb52EQbFSEEMs5QGzGhDobycqpjcBPa23a8fUNlLqQgn/MH8Q3jKrwKIYraRHYANcbFb6BciJi6Ey1zfeqSe4J4r4/KBi48pUFtwYrX5Odf+ofcd8cp17YSmLnG8DWkgMgF4EVcLLbGOmJfXXk43ItbxDFEUTQva0qCrclF/KB84WH1kQG6BBJIgfUDTn/nNUM+yM5Bn+aJnLnDWWVN2HJRkQKvX68IbAGJnYuwnWS4oF/t2rrhFwUaravF1VYuPiUlQueEG+NgCSsFZqHpXEz15+BJzGITudmne3F7+nPCfZ4sSFrHqKwNAeiGxHqCeF9EdDpD2KJAZHuQGuBvz56fdUvMFgTSlfUlBI0vdeld1Xa2V93diovJLd3anT73SwV2Jsa2ox0XdJEbbDDu6h5qr7AyMQ7dl8/5+BsF0a+1zrqWI/ZlSSVVfjrBnT4G9hAgt2JM1AZc4noyr1y19Pq5UpDNK3QoaJ1wbkc3UadbgRZHJlCH9hsl0G0fxSLxExTel0/ir7oYqhUQXzTP7h/PmX+MC0OMtWlBH7e85lU/SRhMr/hwrsl55fUgyI/90DPpdhP++V9vd++gmTYZz95nIJMcf0b9o2rRGBXjgtym4bk1ruBIxFiHeip3htaiG8pV991OsV1BuhK5Po7sREtEYn1BskIQ7/YHfRmR1dUO20lMq7hr2wonL7pCuKTf0EMrRz2lkgHRjQRsZl0+kBIuwG8gnXfKa9JiW5575trAuTyLcQmP9Imv2l0bwIH09nnI3NI+WlhDaYZdCPF6I8wm5TWkwLQCZCcZaqp0rdnAQ3uwfXHDLT61MW3d3R8DTwDo5owxsha92BhDUU4U6WpcyWIcraJaSK0HxgOVC1RqYdtZcxa3QTHSki4E6LAoSrmdVdWfq+7W+5dprJQgYW7D0LglJpmX5A2JLsrXR2s50ZkMicQqOzEVpGxXaDs5j21UlHBJ0zOLtA4Uk9CWvOXjb2cV4bArbnk2Z0/4rl5CSDrL3D/TJVtqYJqes/Qnnuhy8LK2cnRPAGVBUGrzq/ytinxNSgsaZI1SVTXmsFfP0csSmhGEqSI7eZgOy8xzauzy5Su89749X1GKC84rrwdFgsDjNf/jOu748kk+9JFb+PSn7+Luw6tjysU0AwTdmW1ZrKME2gm5VUptdwuDF07GuXOL4BZJkVtsnmMiD9lPIc1RWVjFVTAQnonRTY+8URSCNEWZkdQ4kIKvnAIxoBvsmJSd7opoHu4iPIH1przM5T0oiY28iagscGU9dCDRoUBllryl6O9xvIF1BWaLk7cu8UgXBhaA17UoGSK2DcZ3rO6jfege8Gne00clObrpO2Ti1C4Ll2/kyYqE2VKgFaMA4ws2L29MVEaT4h5WCPe8ZlgSAgg6mnRpxliWh1eFFC3JcjAWO3JfWVRqkbHGi3OyOY94z/CCFjci4tzQOhqPsabYMuYjwbQcr6dMLa0Tk1yqNZdfbCdarECVBrDj3Lc40NDMYwbfWuWsFx35rupBvrPbeVabtk7XJiDd3XJo0FNd0kvnJltsNUnnPVeCBtCRoLc3IOgY/G2NMC4GbELp5mmRpgAOuKEkWD085z3lI7+8SQ6EYonsjR1Ofdccu762g5C2GlTbt6hPGl7/M6/9993/QyjnldeDKHt3zeFtZ2zceoqgl7h8I1864yI1yCQnb6iiUuv4+RboXdCkuXE/s3VHRBSNWYtzN9R2iTLJncKK/EHsqnjJpLbI7Qxv21kfVUXbQlQ3rV6Y/p727Be0QKflTYWX2R1fZisdSWiFHCtvRIiBQs4NJvRc5WVtydoSHQUEm9pZhkDeEKTziqwhCLYAA43TmsbqcPwxiwTdA2ooN8kqQbriE2156FASzCj0SNGndF8bK1ztsjyUSCvwNzI6B6dbUXnLG9/Sl67RHa7nd2ePo3PxWrzYOBersWPM8/7xLYSQ2IaPtQJlLFnbI94bTW5TCboXRMzdFw9cadogJOhmMCijY5nI4TjaP+sN4kVYO+wiVc5qVOnkMvWAmxNp7uJaU65RkuuWlnBZI84U7P/Ck1PBDsWtDNoaTVMxBiLPMZgUCdqqn4EQZLsa1TWmihAYzymndF458I0UJIvO3eh3HVJyqAVReEBK8NeIWagj5eLfBcO+T5v8bZbjb9um8ViDt2hp78p4wtU9vvtH/mx6385hOa+8HiTZXO/yipf+ISeOrmMKKLHXG4F8CVh7zDLzt24QFtQyZVxHWOgfbLB57RL+ZzbxO3qQIDxLQcwoJ1+VFamR/LomnQtxmkKpdnSZRhQLhGn6VZ6M6/MOMYnUFYZEScgdfHjIvSYdnNgW5ciFtlCUoaj47aCo3aUQQlQIPgEE64a8AbqliFcURalaB46wEHTA9sDrGRrrekxXeLFl4e6czUu8YQXmS6zQZ031JawF6xZbmbqF2YbKAVumjFG8K2Tuzu0iBlnI2ZA14+aJv2XI5iYou2K+pIuD+l8Cl4ulG+55y76rSCABeo4OyzRD0uVg+gZDCPAEWVmJuTjGtKJC6VJ5AMxOCqxwE1dS8DLW3eB5SxVlTqbEmzIHODGtcGoOokgGczVdHFieOlLIzCVtCymwZnpenU2LjZ5HQZJL5SVACESuUSPFX70eJJNSU0bESkHn0IjbTghQzroShSvRSvduyHwyw4RTzs4VnC1GhKd6oKQjXZYC611K0FvnqsfcyQ2fvJq/un6ev/3T3+faaw7xy7/4Lfj+7DzQc0nOK68HSf74tz/MiWNOcU0U68AR2VLA6lN2E56KaRztowpqpt6FTbLFACzEuwL8Tt8ppmnutlkMAaUYUy2QVlDR+Iy6Gqd0t6AWKnaRUtJaadIrAsOqn6Nb49NJdXMaJ2K8fplfVVSSriHRdCAdY30thiZTV4ep3isBkBmUtg5CLwYM6sFWRvOkRoeCdM4jXfSc/oIquC20IdqYkGTNQJG3jms2LxssCyqxLrEUOzEuNdpGyZJgfImQsorZzMwj8iSbV86zeOvWoG/GOjqkHchmjSdontb0hHIKrP4cLejIDsah/FhQKYhwNUUoWW16rO9hPIGJdnBFWku64KHSzIElfDW4R1H7rcBIl5A9U/3X46pSYrU7XlpXNSAPhEN21ueqEAXyMiFve3QuahBu5IWlwsAiLDZAVkAeqAFfp7UFj6dy6FvPbawqC61sQ7h8Pmklsp8jEjBSQLtmTWs9qPI88ryCjZR8YTpVXOnanjU2EgvZzu+oVQMXuPEk+UKEaYVD82J1bYV/e/cSXmLwSDDATSdu5zkf/TW+6dufxA//0DNnXudckfPK60GQXjfhw++/GaN3mGxq8JInexskexsTDrKDBFxt3EJYg6YPxXamsDFUXreScUFQxb+GFpodFJjMNWotxQrBd7/iWXzrd38Nv/Pa9/OBv7qB4GRMuhIOLbTedkbr3uGyCoKCVigzziMYOU68ob4YO0SJNHw+bnecGUyg8LcS2MSlAiiFl0C8IgeIxJqoZGfl4/ctMrGYALyuQeZuARZCkjdUkSQ7LvVmy3hKeU+VMprBpJ+uhGw/rE10Kq7y7USSuYVnwjiUC55KLTq0tE5p9Loma0lHvtuEeEng9yxzh/NK8VpZlO+Q0DyW4OUCs9RyykubsVI4s8SqYpyZoLiqQXX+rHoscLgRO2xllvNQSrRwnIHBekz/gjZsJPgbMablYxSuoGsnQzcUnYctYD1JvEcRFwwWFkN0JsPv4GKonkT2Uhon+mQtDz0XuHp2viRtF6V1fInONTLJHbjJFvPVWLLFELsSEh7vuU1kseORvbQizy7dknXy32AzJe7nbkMwMj5VJHIWk1ZeUD9N2cRYITA+7hmUz846L4dpBsPPxVq8WI+VWAIXz/6bP/0EF120wvOf95jpHTpH5LzyehDk5LENsrNgGCiZKuwsl1Th26525JFX5asM+8AFQoEdUWAGBi6vUpn6I4qr1sY0BSaA/QcWedrXPoLnvujx/MPHvsgzvvH1pNbC3qZDNXb1AGptLc2j/fF+MnDPuwRbOXg5ce4TNVqheILITIM2qN7ATSPQmNAja0+2VuRIMu40UZnBeJL5u9MhS9d6EhMYVDrczuiITWIgD7Y06cIU16G1+F1D0APhh+QLXuU6TRYlwbaF0SERVC5WFRt0Q6Iygdpw+VpZLJi/d+BvzVousVsHsHWxoHFqEBd0g1OyJuM4AndQtghRcGUWm6EdYjpWgR0tIFzOtVFXd9mWArTj5IyOddi+pM32w5pD/RK5q2pdoQWLdAYrQcYOQWkjfxD3KqwXv5s7bssRSRd8st0hwRnwO3mRCAE2gzBOMJ4g3tci3Mrc/Nt2pX/KHpWbK4ytkqaFhfaXt+le3Ea3/WHLEVvlW46JtajE4qUWM4H1pnLZexMs+9KtPtpubsga0nFilgAubfHWUnwDaMubfvuD55XXf1SJGrP9xhaKEuOCxqmU3r5g6uQFiPdEhGdS/FaELcATExVC4aahWKR1oyjj7hVlJoxF9XNUNtlCcw3Vdm61/kop+KO//jE8T/Jrv/V3fODDnx8+XjoaI9txUGuvkyOn0BpV/beOSsd6jk3daoPXyxyIxBaLZ8kcPuFc1U/H7kMlOeGZlGTPMNhAZGao3Pss8bYNc1+OXY2mpYE1LBDo0MMEzjJ0bCAu9hNs6+Gd94hE67oIxA8vQF7PkfNKAwiJiQDhOVdXL2fz8pDG8Zz2cT3QkrXFrlj+BjGR4rkF224DIbNBjhyAjGHurozuBbKAhtuq3bQh6R8IkQXzRn1RHpIyD2xLD+bdDjGd0sU7SMYt/pGN801Wl1ESUWjteCUolLodOcb1VcYFJK8YX9XThKvZwIVnLLI7Pl9GxdvOsFLgTUnOFrkl2EyxSqK2k4ljVM2DmptfaUt0skc3aLhCk0KgYo2R0DsYjY9fAVSKNvIBHVQZT6yJ8RhWXMX4qK0UOWLOWWvJI4ueD50VWFOi+ZyHWU0Jt3Ls5nDc7lyV88rrQZB9Fyxx4SW7uO+eM2OGjJVikKUvBK1jKcmCN57AWE7C1M3XrYfP85hggXtvPDr1uqJoM533nD87s1AQ/VoBectHt3xMZvC7Z1cjrNzd/ffXfBOeJzmz1hlXXFUHBF5uCU4m+OsxdhQhNanPefGCpxp/s18tpE6ZWYTOnSUzChuvoxBHpHm8T7I7rBaxYNMRzhrJbMurSBFoH8nxOinadzWwyv64lDfnDurv8wtMiIDcEG7PthZVZpk7ktLd77s8L2tRiaF1og7iqe2qfUmyO0SHgmjdzLSChHXWifYKJgrr2E1UYiYuru2TBi9xCMug6wYxWVB0DzaQWmBKkIOdMF7Fd15HO4ukstbPYjYJ4WJZpjh+AlHy0KWEcCwsxtK/oDFZQRbXt4FEbRmUBf9EF79vKkJit0GSZ0XFJAz4m9PZ9QUOKWx8sJGPkbJwc2eIWpHMcr6UvJUWF3vsXRCQ+xnzRzNkZpFC0DiZkS546EbNtdc3hJu1enUWVG7JA1HFMOVmjJ0P3aY0NqjUVMTDsps5EoGa5yDzLGYxGq6kXo61EiS7Q0Rq8HcgVzhX5HxJlAdBhBD81x94+rjiEgwpLgCpoXU8dUUDKzoai9AuRiN1cayCL99431ld3+/pWl5PeXHwOxmq70hJRwsfjootfpZ3tXnjW7+bpz/ragB+43c/ONT/MRHC0eJ4s+tMVdeRAozBKxSXYMJut4zHVCfZMTLYusjcVgX8vNiSzxdJtoFjM5jcETdeMjMoA9l86Ih0iw5Z4Tb2JUCkVFyyl9M4kbiFtrbLn+R+9RLL/D0p7SMp/pYmWs2BKfchiirJPTObs5FCgWhbuaEFjoR5mlUAEG6YoiKvcy1vX+gUV3lthKjqr1V3UmwYVE/jd8rnYXeu9WYLT0Dg4rUW3EK/A6LSSzRGSeJdwVAMaUyEs75M4fAwrcABfnoZqucAJaJgszkL/YWcMbcsYEOFKGqY4SsIFGa+gZ6fUP6o3AggCM+ktO/tky37oC1p0yNdDrC+pLGa0zqW0jyR0j6W0VgbKK6yTe2BLZCOCJBW0Lh3i3Atxaul2wghsK1izPIBUEoqMR22L1yb+WIwUG7nuJy3vB4kedqzrmb19Da///q/H0zoGkCjLtaTLjk0rxwO4y+PENhQQjL6xWSZtmh53QwdSnRQwI9HpHpRIo99Bxb5kz95OX4RI4t7KTd96ss7b7KlQ6uJbLp1V42JJxFxPnsHDi4PqFz4APTsFV1kBpUwQM2V0OhQgigWehjs5m2Re1e4XKVxrqhMQbbkmEhkYvDL+pQWohNxxYFoZeESK9gVpuUcCcDvGbyeKwo50za1ltZxja1MoOF4h1XCxUKk06y2ULIltHrHJUgpyDXxHh+pxWBsq84WaE5rMYDQgmhbDx8lJTLOMGW9qykLYzqvCE9nrkKCJ9GeQcbjLvBxSw/yHcr41C4DQqDnfPc8Rqa3EQOq0Wmykw1pg5obW4y8z57CtEJUt/aSVi+Um2/zt3fo7gsxTR+quekQgiou0klwG5JyNbCiVPwjClxJTDucrNjLFIRi02c8qvSIWZuAvKlo5LOL6J4rcr8tr49+9KM8//nP58CBAwgheM973jP0vbWWV7/61ezfv59Go8EznvEM7rjjjqFj1tbWeMlLXsL8/DyLi4u87GUvo9PpDB1z88038zVf8zVEUcShQ4d47WvHM8DfsDsSZAAAmVNJREFU9a53ceWVVxJFEddccw0f+MAH7ndfHkz55pc8mW/47qdg5nxMw9XmmphHlbstcml1THt5jD9gNJ8mZ+PAUamZChIROB960A74jd/4dj77uXv5uw9+nk9e/2X+x8vfRrwVTzxvTKQa+E6mXSdUbvHLxvOuRo8dUm7Z5KTuocundiK6C+EKY7rEZYnIDDLWqL6uFg5w1kjngM/2w0Li3T7JLo/+BQGdC3zSCIL1DC+jslKqooxFXytkXc2annRfO0njdO6ImEcUlw6k2yFX5qmbOcIK8uZZWL01FGs6502PbxVtizKeWpxb6gUjcTyUaT5UCbi0tiyQtiT+eoLqa4TWmFBh2j7ZYoAJJ8Qzy/OWAvoXtcc4NKdK2akRRG4pes7HKDF1rllcnuBoEnf1vbUD5vpJIgQ29AaUa+Xn+cDKFBrmjtaQh6WV60nypnSbK20KWL8gb4iCh3L8uqqTOjDKtP5YEEIiMo3Xt/hbGpns9OLAVZftn33MOSL3W3l1u12uvfZafvd3f3fi96997Wt505vexFve8hY+9alP0Wq1ePazn00cDxa9l7zkJdxyyy186EMf4n3vex8f/ehH+f7v//7q+62tLZ71rGdx0UUX8ZnPfIbXve51vOY1r+Gtb31rdczHP/5xXvziF/Oyl72MG2+8keuuu47rrruOL3zhC/erLw+2fM/3PI39V+zFLEbjq0ORh9I4EbP4+W0WbtkmOh7jdTSNExnzd/ZZvL3P/JdjGidSpFRTXVKlnM2CKMp6RIz/5KFj6H7m067iB37sz/iZn3sXv/4bH+BVP/kX3PLZe1Hb2Y4uIlHkyrhCk0y4ii1yie6f4Z9Xmt3OXoACheQsWA0KfkSpa0mxuUZmOXnTks6JsViT0BahZEGyO71tYUEk+eB5TejL2biwhHEWIWXRzlLplKCNWrvlv1Qyk6KvEuM5FpB6EvhMKeJpFLejgyJp1lq8XuoAEdtJxZ0pM42KMxpnEvyedqwPTVfWB2tJl0J6h1oky8HYtdOV0OU5SuE8ErPmvbUucTcb/F3lrRU/+VKEjXzSlWDYFVq/NV+Q7mqQzfnD31vrciSnoQLrIhyDfXm+qNdAw6UpSKagTqXE+BLtC/wTW0QnejTu69K4r0t4so/qD+Kj3vYOMWvtrG+3KXFWnEotzVMp/uYUJLR15zzmMZfMvsdzRISdWtf9LE4Wgne/+91cd911gNuZHDhwgFe+8pX85E/+JP9/9s47Tm7i7OPfGUnbrrv3ho0NGGwwYGw6OPTeS+iEJC81JPTQIRAIIZCEloTQe4IJhGZ6M2AMxnRMMbid6/XbJs28f4y23e3uHcY2dtDv81mfdzUazWi188zTfg9AU1MTffv25Y477uCwww7jk08+YcMNN2TGjBlsvvnmADz99NPssccezJ8/nwEDBnDzzTdzwQUXUF9fTyhkVNhzzz2XqVOn8umnptDjoYceSltbG0888UR2PFtttRXjx4/nlltu6dZYukJzczM1NTU0NTVRXV29sreJ5pY4/7jzVZ56aAY6k6ypdTbSKqMtZb8IASpTrLLD5yLlmhDlYg78qA1l+Pfw+0pXOqR6OChHmMrJnvaL21nZFdBZGsftkWPzdpa2IRNm7IkBlcW1mowzv91DphV2htkactVtAVyFijqoypDvN0phtZaOcNJgGLhjDrYQxgSTLIwGy7YTkOxtqlenepQhGtUmF8hpdY3jPJ7Gak8VLDSeI2gdVkGir9+P0sSWGr9SdFn5iKyMuUdbgo5Jqxpwoz6NUJEqAfljtNrzqv8qjYrYuDG7tInO718ohR0v0zcmSENJY0puHxRDlsnu0PglTNr9tAYpzPja0jgNZjO4YqMqKheYhNxMEUkr4Rn/o2+iTjsCIjZaQspP3A0tTxBakYsEVLYgPqSwBL1ra9yqImavTHRlk4fd6qFiNjLuEqmPAxoVsvEqnawZVwHaAacphd1q7q2yBG61Q7rGCDZcRezbNrLh99pwL2pbomJdk9fK1gQikcfnmDfeVI8Iqd6xLoWg3ZA0IidTwy3lIZOeIQIQYLe6aBSqqgiFl86lBJS6SnufEKojn6fWOE1pXnnyvC7nWAqras3sDlZpwMbXX39NfX09U6ZMyX5WU1PDxIkTmT59OgDTp0+ntrY2K7gApkyZgpSSt956K9tmu+22ywougF133ZXPPvuMhoaGbJv862TaZK7TnbF0RDKZpLm5ueC1KlBdFeVXp+zCZVcdnHuYPJ2VSlnLT+b/GmQinSUrzX2u0Y4pZ4+f54Qyu8yqihBD1++XjagrBy9i4YaNvyRZ55jw77BdYDtXlQ4yIwiVzpo2NRCqb80mEWe0gWx0ZNwDT+OGJYkBlSQGVpHqW4EXtgx7gV+0UMbT4PehwlaWgLXkmGM2Vjb/x5D2Zil68IVFSJLsW2ESXMOl+xOeieayE8Z86kUk6bowqdpCh7tMa2rmtBJZbBZnJ+5/H91SmTA0UzJnQspqAVHD6uBGZNanVIBM8EhKFTjhZdrDaoyXD17woXzhUXSoWqP88amIRaTBNdFwJb4DjdEe7IRJN8gILgCpDAmvsiWx5R7p3lEjuHyt0ItYJHuFjcAFnJRGW9L4yDL9d9iIufm5UD5i9UnCS1OdVUplBFfF122E/BBvmdKk+laQ6ltpNK48/6PEfH+pXhHahlXSNryS+NBK0nXh7A9xo7pahFII181qiUDh/8tAWRKvMkK6NkaqVyXpHjG8WChXzqc7CMkCH7kOWXhVIayEh9PqJ7FrUZRGLBOsVepKGgi1dIio1KaSemhRO7uNu5Ddxl3IL466meef/qA0S9APjFUqvOrrTYGzvn37Fnzet2/f7LH6+nr69OlTcNy2bXr06FHQplgf+dco1Sb/eFdj6YirrrqKmpqa7Gvw4MHdmHX3sdXW63PMSTv4Zo3SDvWsj6fDQyMw5ijPV5CEMmXGldY0Cfj6s8XGIU7phduLWqZ0vR/JFm7wOpeZFwIvamMlXKyWFKGlccM24JjoKiEl4fo2wotasdrSyLiH1eZiN5vyEirsL9qW+asdiVtndpwZf4IA7OYEojUFGjzf8Zw/kqxgClm+kM07LqUpZBl1UGEbFXVI9q5AOxYyrUjWWOC5ueq2GahMOZfcXDMvVeGQ6pmf12WaVX3d7pvB/PFYlPSLZM/Vhh083tMmVWXhxizSlRaJno5J5M5Ed0VkzhyXN3GZ8DXsfOhCzr9y0FKQqpI5tva8DYayBPHejklM9idZ83kbnq2zK54RtDlhHVru+lyNOcFlt6b9RFhjlvOqOrA55P0/VetkmUdkmwkTt+LGFOpWFQZkFFvgZdKj4ts41Z+0ElmWwml0CS9PU/VVgtgik0wuksqY07qwmUoXkxfWQYMTria8PMVPj9uOjTcban5jyVyNNqEx0XtlzJcoZZ7JqrDZYDkW2rHwqsKke1WaBPCuoLV5wPKiV4WrkEkT8q8zKR9CmE1gp81PefeBAKxEvi3TROdGv27BzvtpzJ29kN+f9xBXXfRvvHKcqT8QglD5PJx33nk0NTVlX/PmdS80/bvgyOO3Y9udNujSN6UzdvuOPxYhIJz7sQ8Y0hOnd+5HIXxh0ak/jD9LCEGoxcVp80wZ8o4NlTYLZ8I1EXvtHWzrmZBwaSo2h5fFCS9rx2530Z7Jt8mOM3/MQqDDFm51ODsebGnykBMelqdNIcqoY8KpBUboVYVxow7atkyiakQa/0PSNaYZ1/ielDCrsNWawm5JUvdZO9Vz26n4to1IfXtWiGXMV0XvvxComJPVWjLtpKsNcbLItXOjVskNQsZk6IVshJC4EaNpeVGrk7nXThkN3GpzTWh3WxqrvUiCt9Y55guly3j9zJiVY4JGZGPc1BhLpBHxNJ5UpiSKL4RE2vRnt3vUzWpGtCXxLCO6hDLjCi9zDbeeI8HT2C0pQotasRsT5r4KYTSrUou6/yxktC/paVM3rs0jstwIsnRtzs/UKc3D70PbEsuyiC51qVyUIrYkjZ2JqrQEOBLp4VNDlfeLAqSqBM6KNOGlScJLUkRWpJGuZvprc7j4xp/Sd2RfVKQw8k4m0jn/W5GNAZDNLyuIRhRGcAu7zH3y+8pGigrzHVr+RkYo49tDGq1M+/cqW7g1awUp3X32drqKyg+XU/HxCqreXULFF43YHTQ2Y36Gl5+cxX8fndl1p2sYqzRUvl+/fgAsXryY/v1zESuLFy9m/Pjx2TZLlhQWRXNdlxUrVmTP79evH4sXLy5ok3nfVZv8412NpSPC4TDh8OovyNa3f23pgxkbOximCfzF0Ja5yCIp2eWgzdnnoC3AsfjFsX8H8hZkKdEOpjKs7yNQIWnqTWmN1AJ8bsD869qtZvEU2YsWH6LI/1FqU2rCak+hKh08USasWQi8yhB2U9L4wEJ2ZxNcJmLLyS30woJwq0J7Cqc1lSVOFYB2lSk5HxKIxhRWqlDTEoDd5mIlPNr7xbJ5UOXuvxdzkE2FOQkypfCEh/D3e26lZULr3c4/eIBEDyfP19UhDD3LUOEaZo1MAEY+q3zBkPwgGG0KKGZCtQv6zL++BO0IlGPh1oYQngkUcKtDKFvmkqsx/qYMLA2xpR7Ot62khUaELbyo7QdDeNit6Sw7ffb6iTRKClM8sZumzPz7BRBq80hVOmghCDWksFrSpOsKhYYbs3Di5SpF+hGGWptvKKXwQoUbjIwmLYBklcSKK+wiVof3Z87lrttfYeHSFkQkBImciU1okG1JdCjzjJIrt6K0CUgpM0YsQTrs4aRlcf+dzm2w8BQyVZzjEzDPjV8WRcfb0WHLFMIEX6su8X1ojUi52QAQlbl3Gf+EznNVAHjw6INvsfeBm3fy3/6QWKWa1/Dhw+nXrx/PP/989rPm5mbeeustJk2aBMCkSZNobGxk5sycJH/hhRdQSjFx4sRsm1deeYV0OvfQTJs2jdGjR1NXV5dtk3+dTJvMdbozltWNRCLNS89+yL/vm87j/36HW29+noMOuJGHHn2npC8il9SYgwA/0TKN1sbuPuOtLxm5wQD+efvLhQ+a1ia/KuUhPLNbk57CirsmfNt/+KRbuMN1mpI5wQVd7t40nX0VKtzFrhJACryYn+BZBNnl3jXh8zovPNtpTXUiFM38lSkXK1E84sBoT4qKBW3lBVfmUkXMVsqCmo8aESkvu+ikeji4lVaWVFVj8nUSvUOmXlSHMWoBSmrs5jSxb9uw2gwNljnPzvZRMBYw5tSMBSliF7TSea/MxdIV0vimHEmqd4xkvxjpHhFTdVmQHa9scwk3pnP9CExAUDJNOOFit6aJLIsTXtpOqDFZILjy59XJNFsS5vnuaBnQgB1XpHqGaR1RSbo2hGwv7FNF7C6FoxDCzBuT3K8s8MICLybxKizcqESFBIkaU9Qxuqyz3wetWVLfxNRH3jEf+ZGDHYWgTLlYbUlkS8IQ8ypthteVP1JrBJqmkV5hp37EpB3PpYHIjBCTxtSr883qedqcF5IIYeimrPZ0VhMrGouX8VW2J81zVxFG96hAV0bQ0ZB5RezsbzvTw4J5K4i3r120Ud9Z82ptbeWLL77Ivv/666+ZNWsWPXr0YMiQIZxxxhlcccUVjBo1iuHDh3PhhRcyYMCAbETiBhtswG677cbPfvYzbrnlFtLpNKeccgqHHXYYAwYMAOCII47g0ksv5YQTTuCcc87hww8/5IYbbuD666/PXvf0009n++2357rrrmPPPffkgQce4J133smG0wshuhzL6sQVv32EV1/6FM9TSA1eOOfnAIGypangWuTcUp8JV5EOmZyPFQ0mLy7VUdNIeyXZJ2Q8ber6WH7NpLQyOzVXla0SXAxmg1Z4lUzkZFfQYScbrFGqb3TG52J+pF0xMmgoTybra4ldEs5CAfu50XrNwiBdTeybZtqHVmdJTd2Y8WehtSmTUSS/KH9OzcMkvRemkGmFitgkqyThVsOR6MVsk3eWVlktQYWkr7HYviauC6aT9b8JU7RRS0Pm6gllyHz9/CCz2fA3A1oTWpoisiyZu3dAskeY8LJ47jNbQhelTDIbA5lUxtdZJgfKSvj5fEW4KoUCkfDQIUGqVwirXWG1e6a8hxTFfxRF4FVorLghqtXhDuY7KVA+B2dkfhIrZfgCRcYHLYQxSRfsHAU66iDjuXp7GWSfxnJVwYvASQqaN9JUfe0ZxnttfjsFVgifc9KLduD2VHm8mlqjQxZupYNbHcaqb8FuS2WUQSOQ8sec2RgnTLK4jjjGBVEkF1KHbUi4BZtRWeLZ/qHwnYXXO++8w4477ph9f+aZZwJwzDHHcMcdd3D22WfT1tbGSSedRGNjI9tssw1PP/00kUgupPPee+/llFNOYeedd0ZKyYEHHsiNN96YPV5TU8Ozzz7LySefzIQJE+jVqxcXXXRRQS7Y5MmTue+++/jtb3/L+eefz6hRo5g6dSpjx47NtunOWFY1nnp8Ftdf/XiWwV3imypcF2VbCOHX2onZiJZUIX9cNxKQ7bY0qV5REtVhnnh8JjvutCHvvfkVgNn9lVjgM4uhTLqoWMj/oZpjVtwtaoLqaixICsqPi3jasN6XPMkf23eJXsok/qaL1+HKNsteQpdcQIUQWG0pvMoSRMg+rLacNiKARI8w0Xqz0Mu0ouKLRtzqEG6VCa2WCQ9lCVK9i5S06TgGV5Ps7WAnXZJ1FsmeDirkEVnhoX1mErfC8hc0UfBcuNVhZMLDqxLZWyMyDnah/QUNtA3pnjaugpSVxlKSWL2H06wQaITQyLQ245cCtAlP17Yg2a8SuymFzJhsk11XRwATup0KlyhgqTUokO1e+TphjvGLmlw2sJcncZYpvAo7y9nYlfbljnCQH7mkMkEgHdoLMIJK+iTI+PdPa3ANK0skFiKeSBvfldZIx8ZDIJPpwmdXCrRlIbJlSPCpsjpfNzcAwZTNRnNXYg5LttEMmAbSV2hSFZJ0pQnIEK4m1Cqx2ztscAWGfi3hIlwKfhBSiNx6k/JIVktCzV7O4iL88flBJOXGmI1sThmf9wYbDyISWbsKVX6vPK//dXzXnIXpr33ORWc9kLUZ5yNjZtOOJFMmXSsFyt9pu8av0JUAUf4CgzaL1YvPnMtPdvydKVTnetlQ9FLQkF28tTA7O6chgUx2uHYZn1cGXsjKMrtrwI3YeHX+ilBKgKQVdrvb5TgB3Ehu1ynbUl0XNISidbxyB41fItm3wq/428HfIAR2U9LUCAM/aMQC2zJBD2UirlK1YeKDKrtcXBuHQdU3Kaq/TaO0QoWkqRrQrFAdKYC04biUmSKEWmMtj+P2jpY1TyVrDFsDWiNTimWbS2KLPHq952K1a1Mo0+8yK/T9v17IJG3bCf/ZbE3mSo0Uu6Vg/LHREG5Ykq4tssB5mlBDyizysvTuvb2nDdKQ/kYXx81mLC/3zq2KdCZozhuHsCV7nbQVd814k+i8EhRHygSKFI3I8zWwdLVFqtLOauhWwiPc4JqCqn6whpYmSCXUnMpZOoSxqKhw6dI3aM1T087hkF/+mfcnJ6n8QlDzMaRqfL7RzL32/1pxRWxpB/q0jOmvzTVsJWHps90noandnG5JvAoHO9Om0vwuOyZNl4XW0J4CW3DJn37K5O1Gd3nKOpvn9WPHtb/7T/EfBb55RWlkWxJ7eRv28jasthRYknTPKKm+MVNyvkz/RgDmvjLl+2Z+e/F+KEG3HsqcSS7n2+nou+outCUI18TY+8RtcXvEoCKMSOUNIj8aC8AzuSRdme0y/pf8fKLsWP1oRy06h9WXnb6/cEiliSxpQ8YLTSIosFtTyKSHClkm9y3iZE1C2i7/3dhNyex9LTWndAx0WBJdYqLHbE8Qimuq5rZjtac7axbCMIh7oYymJVDVYVOuw9dEdF7/AOmYoRTKnK/CFqFlHu0DLL7dI0Syp2VMVHSwjvl/rbRhP9EY81I5wZWdm2OYX5y4R2RxwpTD8dMnQssTRBe0mVSLMpsKbEGkTRNuUlieIF0dRoWMhqAcGxV2EGk3+z12vLcAyUqLRx6eSXSBY/ggi8BKFWfaz9wvbImbSfXw4YUl7f1ChkbLkmjb11wsYTQ818tGgYpEOqetdoxG1Jqzz92bUMhGL1IMuS9OeBkketidmd79v15EkKztoK1m/F2WMRWjTdoCQhitKmwTihjB5dZGcHtVoKI2KmrjxWy8cOlI2U7XkYITf7VbtwTXmkYgvFYR0mmXlhXtXWo92rH9PC6NlXQJLW3NBgG4FeXVcgF4FZ3NIdvvsCF/uPFInHKRTvlj8E9V/m/Ci9qdx91xdSvSj9SaXfcaR0hB77CD1ZrEjqcR7R5kCkwqs5u1WlM47S5eRJCqcXAripsXs4uxbZmAEn8XKl3dedcuZYEQ07bMMbsXdJrZuvth8p4mtDxOaFkcpzGJ05gk1Jg0bBeWicoUliyIrMr4uEr96KWG8OJ4VgvtNCcB8V5Q82mCcGuHwAdhmDYiy4pod/4ipa2cqVYqjdWURAmNcvzAhIgg3sPqXPBSa3p8pAgtM0UbQ83dyC/UxsTUZSBGZlGWOS5FqSDUnCa6OE5sbpMRZNpPSs+c07EPjHtO55nktOMnMtumQgGWRAiJSHh4dmFyuglSyavmnNYlc71kuVp2fn92osMY/fuZ6OWg/EfQ8/17wpK4vSvwbBAtcbM5XdGOvaLNCDFPGXNka5Kbbz2OXXffhC++WExre5JwgyY6z+1sBehw7VSl7JxXqLWhmkopQk1JZObeSUFISFIJl3TPGKqig4lcGPOwF7W7FmBa8/u/Hc8hR23dVcsfBAGr/CpCY2N5wZVBtk3WiSwJz28kMaQOL+Zgt6agCCO4EXwSz8+V6bgIbLrpMO579DQOmXJtyWtnTDwZ536W/cGReGGrs+mwo00pvx9L0KcyzBP/mmF8JhnTifJw0h46KRBtKdzqEDpkoZVHsiKMCBmfhldh6K/s1nQnk4jJk9HIliQqYiM8clWgiziXMxFsXkUImXBzQipfa3BzXHcIU7RRSFk8pyhvLPnfk446iA6O+8z/tSVxWtJ4lWlSNU7B4ulGIN4bhKeonps0ZxTxxWgFTnOaVF2HdA1tAgssz2hFqQqTMK5tQaJX1z9hKwUDXk2TqOu6ppXGmJZ0RjvpKsJPlTD/Zj5UxsQmXYVoS+FFnFxZIPC/Fw3hQu1CJjzsdl+DyddGpMkTy9zfTE/hFhc3pUjXFPd1FUyw3Hwg90xok9DutHpYvunWy+QxSlFQyFNVR1BJz0QeAsLPlURrqntU8I/nz6WyxvhEP/+8HoSfB9gdr40UeGFRKFSFQCZcrHRhcJFUGk95JmKwVFFcIUCa34zomAif7UxTEbLZdIvhXY/vB0IgvFYRevSo7FbQQ6dHVQiEZRNe0ITbp5pUXRSnKVFQrkRjSnukekQLfpSyQ+BDTW0FfYf3YvHXy4pHRglwY07nnBwhSNeGsZtTJqS+04hFYT9oZGucZUplz8925bdxK2zc/h043LQGj2y4uheS2K6hk8rmlyiFSJpdv3A9RDwFFeUCIbRv3w8ZYtNYyOTHJNKIlDISQftjLGe6KoYObXXINtV9k2lI5+bu2cZpr2IOTlJjLU0R72ORrBakK014esX8JNVfJQ2lT6lNNqZWFrWdzYcZqSO0wG7XeDGFFlbX0ZNC4LQpZFrjtHbT2VGmnlUnlOrST3UQybQJv9Ya0hrLTea0Sd//kurRIYBKa6z24tF92rFMZGKHa6HB9nPRwKQsJHuFTdmXfBRTjTtewzIbolCTS6hNFYxDZuRaWuUFavjfjafRjm02GlKwyYRhnHvtodT1rCq4hpOJALV880Y3TLOdB6oLGFiMSwFU2MFRmmSsRPBMHpQtO7O4ZOajYdd9Nv1uY1rDCITXKoJlSWp6VdC0rK28WSZVJHpLCmRrCukqLD9UWgFurdmBq4idy9qH7I/lnNN269TV//1mdy446wGsRDobTagxCaJuZWGCa+HgBG5NGLcqhEy6hBY2mQU6lAun1ZntuOehw6GSO/N0tYNbXdxhrm1hNEulCS9tzwVuZAaU6S9kG+3ID+sv/SPMVA/OE7M+S0hWO8r4CIpFwZWYQ0Gb/OOWRMfCJkzZX3y1EIa70TctSqWp/DZJ3Yp2s6jEQmZhk6JLQ31m59+ptHtmqH5EqdPqQgU4bZJ0ZYk5aI3TqrJag1R0ucESfjsVEmilyvuqhChqzgJIVzjYSoFlm5zDfJOg8IWQFKRrwp3TCzSdGUb8wXVKknUL86WE/5zaSY21MEF7/2iOIxBQjjCckSXugsBQdlkJRaitMxtLRvZlyJIzx6xGk+wtwhZX/vkoNps8ElkiOGXTzYaZ/UbSM4w0XQkurZGpwvsh4zkrSeaICtkQCzF+i+G88dYX5fv1ta9i18JT4GmmPTWbE07fJVvPb21D4PNahTjvov3MRqrIMY0phld0p+NDpIzZAW2+GLs5iRexiybNHrjbeHbbY3ynz7fYfDgbjB9MujZCqiZkXnUR3I6LhKDoYi58k4zXq5rQiN5MOXISdsQ4elVVCFUZQtVEUdURM7YO11dgQrAp0n/GxGeBSHrZfKai0NowgnQ7wUchky5WUwKrIW6Ei08Aq4sswAJypshS1+8YcJI9pPFCpryHVxlCVTjGL+OzLABYrcnsdVTEwYs5hUmmpS4LnQNohDCmPMsn6/Xb2W0uTouH3Va8dpiVUIb3L68fZZceg/bn1rvGRDO6VeGi889+5nkmVyjvMw0kqx1S/SvwouFs3mHBdDSGOULprC8RV2G1pHCWGl9kUeQF8BQMGiO0MoItK+C0JrYwjtXuZdnt7Safj7HInDSmhpYKCZzW0jXmMt+O0OY3azfEsVKer4kJNt9m/ZKCC6BXryrGjh4AYRs7pbHbyvMlOq25IpUoUwFBpDIVks2G0I3aWSH9xoyvC5/fEv0WTVlxjWAXlqS1OcHM6V90brOWINC8ViEmbLkeR/9yR+685cUCn4fG7DZlW5EM9cxDJAwZK+SZKBRE69vwQtIkFwsYv+lQ/nj9USXHYFmSq393CJdd+Rhvz/zK1IPyNHg+a7gtsFOKyliIBp1zyGvAjhvbvtmdC5Jxl2efnF24mHbQQlRYIJM5LacYf18B/POtRBe5Zb5pKV3h4CS6IAXV2vDs+fPIEqLmLWJaaVOCJv+8TECIBZo82qjMjz6z0fCZKcwhnQuFFsbvIX22fDCCRwkgP3k85YEfOdeRpaJgGviBAPn3L6N1afx0ipw2oDHRc5GlCpa7JGsttCMQLoSaXZxWVbA7N4SuOldl2NcqtTYJusLfACz1Wk3ZkqoIXsI1xM0i774IYcrSRG2ilRHiKRchBCPW78uctlajjSmNXaRScj5EykO3J5HSwvEZ4fPH2+W2xfU6a2L5/WMEdbghWRDso+Ou0ZTztNvM/clE9lldBHYIQCuN05ij19KA7CZr/Ir6Jr8jQcXCFC1Dw6jMPiATKg9YbR6Vc9vNbLSGhIuK2KgKBw/MhtTfiGo/n84SEBU2rV1odAXabUbYdThn+bKWbs3nh0AgvFYxNhw32JDPppWpGouH1r6KH7JMCfj8aqZCQCqFqowgS+yUrJTCSpmd/MH75krJeJ7yXTl54fNK89JLn/DZrG/Nji4DKVASZBpkc4LEsnYG9q9hYTpJujbk1+DK+FW0T/5auMvUSqNtcg+4EGYBSIvcLq67YffdTC9M9olgLYj7nIQl+vF9NBkTXafoLd/RT8jK1VPz56ZdZVRiO88Z4gv7bA9pZYh/HenXPBPZBTzDTJJt62lsQFWFoSWJBKxECi9ktJlMWmWnGl/+wpGuLtRkUMaMaKUUdlvnAoTCUwhM1Jm9qLgmqzH5gQhTllBbCqW07zPVfuVnz/gb/fDo7HR6V6JaE1jNiezuX1kCrzZKtG81jz36q4JrfTF3KVfc+CRfzfimfDCMDzutgc612Yp/1xR+r9rfdJQVYJ0fNVPexUMLk1yuQibs3bYstlp/CG/Omd/luEvNbPS4IV2eC9DUlNMupQfVc5MkayxSNTbKNhaacKNHZGGb4b/Mu7DVGEc3J3Bro3iV4UJ5IzSsiJPQQE3EkCkXM5drcsEa2WhcDyELTYQ9Ovjr1iYEwmsVY/SofiDAky5Oc5zYskQ2WEJos7NOhSX2klazSEQjUBVDh2zjolIgPA/taZNJ70c1WQkXpOSWvz7PZZdMxXVNTS+Rctlo4yHsefAE5i9p4t7bX/MpbjKLlYFWGqkw0VDaaAjLFzcTAUKNSVQkZMLEoSR/oNCGhTyzkJuOTXRgxowhUl1oST4MKXC6bBvtjy3eP0psQbvxMeVpU9lGXp6GZJcw1whfOFmiwFyY2UFrDaLEblvb0gg+yAlGrTsLrrz/Sy3wqsKQKVmRdtEhBx0yJVvyuQGE/z2lQpp4X8cUx0xpoktNJKZhFC91l/yCpZbh9dPZT/O0F1/jzkJKdEhDwsPKp+nK3Nu0V7Do6coIbmXnoIqK6s4k1k8+MYtvZy3sWlvODCXl0bpeJVVfthUt/5w/n0wuVdbk/V2DHIp0Lj1tOB+B00/5CXvvMZ4ly1s4+rjbSLWmigvRzJjShZYLIQXnXXlQty4diji05RP+KkzJGa0QrsBpdgk3FX7vWinwlG8G11gN7WgJbk3EL48kEEnPmP20xmlK4FaFDWtJflBV2hRzzVkaMP3aGWYXjLkyZDNh0nrdms8PgUB4rWJUxiLUVdg0f9mA05hnCskoJkmPUFKh+vcykVC+mcEsMkbCadvBs4UJsPA/9+LGfLOgvtFvBzgS7YT5aNbXfDTzSwQSEbaN2UyIzouq1uiIY1gL8o8psOIpVEU417YYhDCsCykXre2cvyIPMukZzkS7xOKSyeuJOiASuZDsEgg1pPCiNrS1I6SdCx4RAuEW8jjqUJFKux1hSfAKFx230kaFLEIrkp2aa+nzvOXfA8iaCUtdySxmktSAPJYBpRGuKe1hoikNybInwV7Rju4fRUsTKedFILoMZKI8E4nnSKO9CGG0YkWWdR+BYXwXFJhQM2VjRNqE7WnHNnPMMEooowuVTFD2d+5bb5tLXG1rTXDj9c/w3Asfmw/K+HzyIRBEVqRJV9uEGjsHM2U16rx7mAleygvCLAmtNbguWOHcs5E3L7fCpqoywkknbM9evg+5T88qzjx1F66+6omi5svsJRMu6aqwyXPTmv0P3oLauli35r3r3uN44J43zRhsaBgTMVHAPpI9bFqGaHp84BJuctF57Dn5Gx/Z2E6ifwgrZeG4mlB7MheNqcFpThpTtr8ZkWkP4Rmfbcd5KUuQrgoRWpFACMGRJ2xLqASB9tqAtXdk6zAalzcTaSzOwJyxw4v2JLq2ovPi4L+XrjY1soQotE1nTXbZf9BhJxtaXLDQdrq48HfhsoB8NmtaSXvgM0kUjEpro+W5Xo6LMZ3Ek+FsmfKMUJKA05Ak1TvSWZD470XKmDtTPaOElsVL+jcEplxDeEUK3bMaLSVaKV/LUNjNHRa7rnbjGe0rMxx/wOm6MBrMZqNDmLgKWz6ZLX4NeX8OXZjEBBgNTymzkPtS2tx7DyyLplExk2gqBLVvJU0xz7zxt/cJUfVNouQCqoXRHHIaljD+uw5+F+G3FUoTWtxaWGZDAykXkXJRlWFTS80yfjsrT7nN1+S0EAgBJ/10W5TS3PePl3ngzteJ+9qdEAJiDjqeKlt0FQDbwmlTJCstMubDogjJAgEMmMjPcpsIPxpQLG+FUAJVETHPq1JIT/HLqw6kd68aNt9sWKdFepefbMwnnyziP1Nz1S8KvnFPQVXuGdda8++p7zJj5lz++Kef0qNHRblZc9xJO/Lw/W+RdhUrNoqZH07HZ1fCinFV9HpjhdFkM89eXjvL1dTOWETr6F4kekUJLel8L4TSBRp20d+b8BOXLYEbc7Db0gxdv1/ZOfzQCKINVzE+fXcu4a8aKUcZKcDkCpWBwESWZSLysg9b/jbUlzrSD34wJ3WxgGtd0i9VlLtP+5Q36VxIcuZsqzVlCh4m/HIr/kJixz3CSxPZ8hRZeBqnKU242SXUlMZOFfbX6dJSoCvDpoJzZicvpcmVsiwTGNFxbl1FWOUdViFBsmfEaJ6eKQ2f2dV7tiTZM0K6LmxYu6tMBWTl5wB1F8JVvrnLfJ9Wwvjc2gaGsqznA3vXcNmdP6NW2thNubIvbqVF6+BwtixM9qvXJlxephWh5hQy5ZYck/YjAJECpzGR5YcsaupsT5mo0pjjV+wW2Xuc6V0JIwj7D+lBKORwx80vcPdtL5P0cqbqDLxQ8TIv2bH52p7G+KCKtskMMKPxQkFkYWYFy/9ZFFxvSaM5J+ViNbRiLWtGrmilz4Ba9t1rApO3GllSuzj9tF34y1+OZqONBxGtCBGJOjghK8dAT25cmb/z56/gysunlpgxKKW47b5X2eu4m2irc2gdXMRHm0HGbOvkbQo6NRHIaIzYm19hNaVIdExw7w78ZyQTiOTFTMj9Y/9a+wpQ5iPQvFYhPE/xu5/93ZhuukiN6I61vtMOiszilf9Bjkm++0tq8WsVO79UiRUhzKKTiaDMLDLakSbiKe4RibejwtIUuvMUVlJB2oWwZcqilLmwBlS0c8i9yDuuqyPQlIv2EmnP2O1LQAuBG5W4vRySNRIdtokuydVPwpYke0eMOa1INWpkHqN3urz2pf1hP/DgqTzzxCzefWcuWmvGTxjGpB3W55NFy0imXdYb0IsJowchhOCBty/mmosfYeq33xqSViFIV9s0VkicVg+n1SO8It2paKVMenhSZqsj50erCaWxm+Psuf+mvPLoe5SqyCTAfM8pDx01EY8CssKlIxYsaODRqTO49+E3UVXGlGvYNvKi+qrCePi+wTzhmrUQ+GZnAcgSSldG+/ZC0gSMZIJMMndA+lU8femVFWCeQixr6rQ7zxw//dIDStyJQvTsXUVcK1qUh5QCpRRWmaAkrWHWrG/56qsljBjRp+CYUoqjzriDbxasMAJDQqKnXdbULTxNqCFddr3QWiNjUUJzFpPYdBB6meHZLLvG5EWOamHK4WTHIAVKab6YU1+uhx8cgfBahZj5wkcsXbDClPruArqYhuQqk8ScMelZwizyeUzaBQIsa+/LdIoxZ5TTvvyQ6E7jwSQyF2p5vrmwxBzyBQm2HyLvM0j0GFCNbUsWf9sAKLyYhUZjewraFTruku5ZgZTSLDQdO7dkydpY2fsgJV5lCOk71nVagaOK7mQ1xpzW3jeEV5EXjJAJG8/eHpH15RUzreCHypu6WyVMMP5nk7YdTZ8+1Rx1/HYcdfx2BcdHj+zf6Rzbtjj/ykNZceytvD1/CYneEZNUm1Q4TWkiDekC4ZA/ZpN6oE1BUD8fSiZdk8agNNMemWm+yzJauQZk2sONOgXaTjEIrbnx7y9CLN8f6N+XlMpeR1dFcENpwymYCbawS0XA5S2o/j1UUtA+KAJS4sQ1Mu4WsM8ARf1rMaFIUvj9ZH4pm++9KRO2GllybhnEEylOO/d+lixpBkwkLzpj+C8NIWDWe990El7X3jLNCC5ASbCSuuv7XIQqriikxJm/nOT4IbgRCydeWpNVtvSDtox/1a10Cu+h/z2szf4uCITXKsUH0+dg2RLP84ypqwQ0oKMd1PuUi+iQ56U9jdWeQrmWCQHvKMDARI75nwlAJFx0RYlyEJmcsiIag8CEU9tNCXRF2Cw+uusfjoYCQZMZ2/BRfWnu5TCn0v8RCUFoaRz7W5++p8KYS5QlkZ7qJAQyJd3L/rABFXVQUcdET2ntB3L4BLuZBVRrtCVoHhYuX08qr9/SB/2jlkQ5xnTXcYEUgGVLLrnusC6vVQyHHrc9H53wDyq+9BdNW0BVBeWs/MJnRrB02jjntUam/FIa2TGLohF9xTsscxc85RfI7NxO2wKFzLJ6mOvKsqZWnTf+jH9Q+A+0TBp2+kS/COkYhBsUdhcRrQMH9+Bv9/2cS8+4j5nTPkDF0yAg3KOSg3+5Mz89accuJm8w7cWPWZTJx/qOKDbdp1/6yGx+hE/+q0F4GAb8Evdb2aLTBqsoXNdYAjSkIxLtWDjNhXq2wORhtg+IYidLcIz4EcWWFGyz/ZjuTPUHQyC8Vgf80Ggc21DsQHZV00IYR3NFXuixUohEkRwe/69MeXiOB8V2QlIYihmfeUGkPUikDfNBh+gqDchEoa8ts9imq0ImlD7pQjzllwNfueJzAthmuzG88MU3BT/KVK8I0QVt4GlUzK/gGrJMCLzbsdjkdzCCSlPtVvsLoLYlyk3j1YZBQ6rWxq3scO+0qZXV5aJQDH7whRDCBLlIkQuS8DQKTbgjX993wObbjWbn/Sfw/KO+z6ETB1NnKFuQ7hE1pTp8pP3IQqfRj6K0JEKVnrDAsIEYhT6nBXVENmm9hJ9GOwKdzoVi55u1O55REGyizXMAIKUg1qeK5Z6LtCxiy/xNkDaLeUfTaT4O+elkbNvm8r8cXXKu3cFzL32cb4E1kKW17eycNGy88aCCz9rjKdK+X1Bk8qyAcJMi0avMhsoSJOtChJcly2vNrW2osGPGF7OJ14RIpMKmUkFagwXJnuEssbdQytQA7DhwwE4pLFuy30Gbd7zMWoUgYGMVYuxWo/AyJj/XhbSb5d0Df83LEKnmPYciVVzFz0DjC5W897k32lRo1rmoM5lwkc0Jw+yQoedRmnSlhVsdLsj5USFJqi5s2NvTClUZxqsI48ZslDb5SF0Fn3QMAKmoDLPT7htzwkGTO21B44Mq/HP8itK2NFqSk3Pea8ixX5S5J1lnvn8f0MY0qCI2hEMkeoVoHxDuLLh8xL5tM/lzKwENiJYEMu0Z81x7GtGeRktI9YnSrBQvPPvhSvUthODM3x/CSRfsQ+/+tUYjKfMdKEuQqosU8l+ajvAqQ6TzCoS60ZCpk1VkPipk5UymJS6nlS5u9itopLPktvi5SZRgn89+1iHYQ1qSodsMN99l/rWEIO2zzeQHaWSw6RbDmbL7xqXH9h3Q0profNuFKSlS6tsQAsZs0J/1RxeahfN/IlYqZ9EINZlSNSUpuJRP1izo9DvMvNcNjWhPkR7eC7TCq/TTScIW8YEx2odV0D64Iiu4LCno0a+68DoAGux2j1jY4YprDmXgoB5l7s4Pj0B4rUJsvvNG9B3SM1cxtsMPPPsu6SIaW3MHivl8Op6XLemR+cn6DvmUi92aNA9yfjFLpU1kmSabBGu3e6SrHVK9K0j0jZHoGyPdM4aWEqc5hReWpHtE8fpUoqujqLoYXk2k5I5P42uSvukye21LsnRJMxuM6Ed1JERoWZLKr9qo+rKNULMiXRvJMXJYEi9k/GXa8fO4wo7Pa1ha/xJQWHXY04aB3jb5K/0G9eC+q471TSl5i4N/3fCSBJFFccKLWgrSBgrvbqmLC1TUJjm4mmTvKOnqEKnaMIlBVaT6VmS/+9tve7Gs4C8HKSX7H7ctd7x8Hne9fD4jx3T2kWXgxuzSvhNfgKkMk3nEQlVF8epiaFsi/VU1VBnG650X3p0Z9kqOX6Q8rPY0VruL5Wu4Jhm8lFTUBVx7ti3ZY8rGxX8XlsCNWrnvNfv9at57+ysuPPN+UsUIsL8jhgzuWTQ4Qzm+9kfn56R372ouunj/TudEIqFsiZT8kyRQ9a2LleggwLRGxj16v92IrYSxurhu4f3zPNTS5ajmFlRVhNR6fQgvSBTlQs2HpzTDhvfin389jmOP2JptthzJZusPYNeJozn9tF24/9HT2Gzz4V3cnR8egdlwFcKyJJfe/X+cvsfvSaRK09YIgPYUuloVsLyX97WQfbiF6xlaoKSbS1x0LHQ0BBHDFlFsMbM8kxeUqLKQypQOt5IetlK4ERtCstMYVNTB9RRWW6G5UWACPFTENlGEYMyhAtoTKS477xGuuO4w5MeNRF2VC5D083484ZnIRCnRIQtPmGCBLBO+JXArLKy4Zxa+Dtc2BQm1qX/mByK4daZ0ipSCIYN7MLxfHXXvN5LoFSLZK4y2BHa7R7Q+jtOYzt5SqyWZPRetEfE0RLuoC5W5PxUhVMeUHj/ybvGiJuoXNdJ/QF3ZPspBSknfgXVceeORHL3vjSSLmH1VpETZ+WwjUz9NtKVzN1IIvOoofWuinHjaLtQNquXXZz9g/FnZ2mwYM1Se77Ff32rql7eUvZ6Mu9jx/Fwy/7lV5gMtKfhtaHxTZHXYLPApj/FbjWTnbTfguupnaGtOFj6XWmdrfRV+bv68985cHrjzNY7+2Q6l70k3sM9u43np1c86HxDCRNF6mpASSCGoq6tgv/0nsOde46nsyEbioyKlaYkItKX90kAGEqha6KFdDy9mtLrogvZClhIhjKuhsQXV2mbcEqk0WgrSQ3qR3GgAkUVxHC1IdaxK0AGWFPSsrWDEsN6MGNZ7pe/PD41AeK1iDB3dn52P3IYn73itbDsTXJEy5TUsiXDdkguCxgQwiETa1KnyWQYE+NWDZS60W4iyUtBKKEJgeA8FmF5k5zLkeVCVYVTUwW5OmJK3ElJVDla7i93iR/oJ0FhgWyhP881XSzjxsJtQboc8tcw40gpPCFSIrPD17Nx2NjMeL2r6k65vFpTG1EjGLNWcRMdChoXDH7tSmj33GIdlW4S1wFoQp2JBcaZyAVhtqazwstrTRBY041aESPWtLAw/Vxq7KWm4K4sllUJuofYX71Ty+2sAALU9Krn78TO46oJHmPXO3Cx7fW2vChZ3x3GX0VJ94YEy2vjC5gTb7mbMbH+67kj+fNM0Pv10kZkDMLB/Hccduy3DhvbGcSwGDaxjpz2vRZUKpvE8U65Fa0TKRcSTWT+Wti1UNIyIhrIPRFbBcywQ4EUcRCLNCb/ZFYCb/ngUPzvlDtJ5RM7CVWWZNbTWPPbwDA4/dtvvVc5js3FD2G3nsTz9fGfzr5CSLbcYxu8uOhC7TFRsPk46cSduvPU5knUh7CJBU5YH4UWGTswqQq8lhIC6aqitQifTqKiF2yOGbHapmp8w5lwpcBrTpHqGSq4nntLssd1G3Rrz2oxAeK0G9OrVNZll9tEVAlnu95VhCUi5iFTuXOEa/1TmR9yxjEbOHyQK+kEKbNeUjrc9Y4bUGYFXbvcuBW5VBCvumqjEdgW2Y3J8PGX8dilDYaNDtgmUaC+dn6IxgSjKAiwrL/LfhFRbCTMuFfbLmhTjLBQCt2es4BpCCDYdP4StJ68PgGNbuF2YkEQHtc6tDBlhJoQfmWnCzp3lcZ/RQuPVlmAQAURaYWE0wIfveJ2f7D2OTSYMK+tw7w5qamNc/dejUUrR3NhOLBbBVYrdD/pTF2o72QABZQtTtdjT2fIqTc1tPPnapzzx0gcsT7bRe+M+bDFmMLtuvQFjNxxYMO55ixpIh8BKZjotnL/d7gdctCcR8WShWc31sFraUa6Hqo5m+1WhwkoEOmzzl78+z++vPYxhg3ry+AOn8vd7X+eFlz8hmUjjJDwS8TYTul4CLc1xFi9qZNCQnt24s8UhhOCcM3Zn+NBePPjoDFY0tAFQVRnhgL0346hDJ3VbcAHsf+hEnn7sXeZ8tYRU3yjSLfzaZFIZ33WZoJrMuIiEoDKMtGxsJy+4Ju0RWuGRrnEKSbQz0JpQQvPqo7MYO7w/oci6KwKEXlmj/I8Azc3N1NTU0NTURHV1ddcn+Ph89recttcfu1xPvN41VNTGqKl0qP9gAUTCWQocUZBx2TlkXYOJsAs7nT4vKoj8sCkvakxMSmhCadDL20BovD5dCFzfr2C1u7mcEJETjOaHY8L9tSNByKIaV0coAV6FDQhE2sNyc4Enmb/KEqQrnc6Lv9Y4QuD5u9hQyGbPPcZx0ok7EPbvy2kH/4U5Hy4oPwZHkhhcY3K8XENsWkwwCVcRWtxmqv/WhPCqIwWFDvFZL+y4ScoVnsL2TPL6phNHcPEfDyMaWwkGhC5wxPG35jgvi0HrbI0sryaS/QxlNkF1m/Vl4dIm8h85IWD4oF7c9NtDqKnKVbL+4JP5/PK3D/iM+rpQA/UUTksa2ZbCamorO2ZPgGhuxR010CzEHSAEPPHkr4kUOXbHrS/y4F2v4xVjhMnDnf86lf4DV95kmw/XUyxY2IDWmoH9676TRvf0YzP59/1vopVm93025a5bXqI1kSIxIGbIdDXmWW52fQo2Y7ru6reT6hkDS2C1GmsMeRqpZwsSA6L+b8uH1jhNrolA1FBVE+XOqadRUcLMuTJY2TVzZRAEbKwGrL/JEHr2Kf3FaYwJZeMJw3j0+XPYYvsNTcBCKoWK+YS3/gNdNteqaG0onQsWyX9hgiuknyMjXY2nNMISCCW655jXGEZr6BQBBphE5YiDFLJrqeVDajMWkTENkjs1+9fzeR4LxqIZ0qeGfz10GtdcfSjX/v4wHnnoFE49+SdZwQWwy4Fdh/sKV7HNtuvjROzcVYsIf21LYzIEQk0povOasRoTJrqzPY3VnM4KLjSQUtkF9v0ZX/PHSx/r3k35jrjonL2zm5NisHxfl5uf/ycEWIJET4f6Zc0d4wjQGr5ZsJxrbn+uoK9hg3v5nUq8CgsvLFA2eI7ZhGgpOmtcHaABqZSJyC2RD6k1vPXmV0WPbT5xvfKCS0C/AbX07V9bZhTfDbYlGTq4J8OG9Oq24Ppk9jfsttVl/PGKJ/jmy2V8+/VybrnhOdqTLtKDinntOPVt6HQarf35+AQD2ukcEZqBxo8MtSVeSNC0foxkVeGYVEjiNKWJLUgQqU8QXRSn8qs2okuTWQHX0hTngtPuXbkbshYgEF6rCZff9XNkCZOCEHDZ30/gutuOA2CLieuRrq3E7VeDqgyTrrBJ94iS7lVJumcF6SIVi01HhW+TrosXstAlHnuBcZorTMkNxxKmFIPACMJyAkwIZLuby8kpAW35SdO6e/JLYxJRZRdMHlZaITI/cF+gn3P2XlRXR9l8wnAmbDaMyorOO8g9D51ITRmSVO0IWrfozbOffUWb6MI/5UfuaaC6NsYvzt2TW/5xIpPGD8dxwYJsaoFWJt8sc0eV0rwy7SN+e+o9PP7Q27S3dWawX1mMGT2A6393KJFQh7w8pbHa0ibtsC7cqVyMkhoVtfBKmN88pXnp7TksWZErSFhVGWG9ob1yjaRv0vWfdTdmG+2hzHiztoW6quIsKr6W+/lH81kwb0WnwxuNG8zI0f2xSkXVaZPrJYtECq4pLF/awukn3oHqQEyc/b8tSdY6tI2qJtUnSqpnmHSlZcinhTDflT+/rAEm89eWuL4GnaqyUCFJ2/AYDeOqadywkoaNq2jeoBIcyxB8JzV2q1dYF8zHJx/Mp7U50fnAOoBAeK0mjNhwIH9/6Tw22nJEgblrvbED+dNjv2LLnTbMNY7ZuL3CpGoclNZYWEikLwAEoiKC27MCL1/ZgU48ft6AmAlLJvfQa99Br2UuBFymNYMH96B3huVDCOyWjEOtyEKWoYkq42PI9JPxXWTMfl3pc8IfaJelLSBbRbgmGuaPVx7KRhsP7qJ34x+44rbjcEokeDdtVEc6w4bekUi4GKTgkpuO4t4Xz2G/oyaz/pgB/O66w/nTX46CtIcOWcacWxFCVYXwakJ4IUkm4W/GG3P4y+//y1F7/pGPZ8/r+nrdxGbjhnLwfpubiD5P+7lVGhWx0X5CeOYZyEAV8yN2gNKaT74s5Li77Nd7Y5XYmHmxLnLA8tv2rS20HmjDrWi1pbESLo/c8TrHH/BnzvrFncybuyzbTAjBZdceSv+BPfz35vPMmPY/bCJ77j+hW2NYXTjv1LtKbuCyFgUpC8zOqRqfyNgyfKDasfEitkm6twxtWbomQrpHBC0FblSQrszjkrSECXAK+ZHEcZV3c0p/J++8+cX3ne4PgnXXW7cOoP/Q3vzhkdMAcF0X2+c8/Oj9eVzx23/x3ntzWVYlSDhArxBOi4udt/kvfNwEqkcFcrnvS7BMEqLQZref8jyUk/OnmAhFkeW5y5yD0lRHw1x0+YH830F/zoXrugqrMWF8IvkX1toQ3rrakKmW+EEaU6ghUKXCyvrHrGRn6qeO53UbGm686jA22XTodzmLkRsN5M//OoV7//Icrz37oQmPtySjdhrFay0rOlSBLj8iKQWbTRrZSRi+O+tbVGWos/AXwgiy9rRJBfDR3pbkglPu5vZHT6OuZ+V3mk8xpNMeU594N8/MLApvemYRy9S08Zt0Bx01mKEDe3LX9cfw22v/w9fzlmc/j4RtwkjckAXx0rmLGiAawk4D6ZQxg4UtBCK7QcnHh7O+4Yzj/8Ff7jqJ/oOMD6tXn2puufvnvPLCx7w07UNaW5MMGdaLPfbdjDEbDezexFYjvvlqWZfpL3ZrGshZC7QjSVdYOO0eSGnImQEdMX7ZggrXDsR7lU6RiCwr9JlpWZpIupSFaG1HILzWEDKC61/3v8mtN05D2pKmAWGUDZlHPNzolnzgMwuSF7awXEV8QCU6ZBFakUKmFapnyA+bx/im8vM8CvxT0JxMcdWfnkT5VDUaUDEHL+YUUgF4CtGShIowwmebkkV+ABpwo5ahqcqjpHKrQ9CSzlYcLgYBJCoFoXgeU3iJdlKI7yy4Mhg6si/n/+lI2tuStDS2U10b42//fYvpzzZm/SduROCUiTOQUrDNpFGEOwguz1P869EZnYM8IHsvVMQG10P6t08pTTye4qlHZ3LEiduv1JzysXRZC62tXZgi81lYpKBvTSULSZqw9xKwLckm63cWBkMH9uTuPx1HY3M7n321mLrqGE89PZvHnngP1buSyLcNJemgAHSHQAwr6eX8qR2gPE17e4q7//YSZ1+6f/bzUNhmyu6bMGX3TcrP+wdAV4JLQFGeSWVL0hUgPLOhRGmjwbkKZZmUFulpwkuTiJSF1eZit3mG2aVHiHi/CKE2TbixQ4XsEl+xELD5pK5JitdGrJsidx3FJx8t4NYbpwEQj/m5Vf5iZyU10ut6M6wqI7QPqUYgCa1IYaXzTANSkK6wstn/pRgXhIbPvqgnPqACJcGrcPAqQ53rfAmBrsxRC5Wi5FEhmasCK/L+CoFbYdgfigW1ZvpqHmrKkJR1UEvBfqvAFBSrCNN3YB3RirAJWMmfhyNww8Wpf4QQSCn46WGTOh3b99AbaW5OlDaXZcypHRz9WmneeOmTlZ9MHuxumAAzsCxBOOxwxUUHMGXy6JK+ISkEe26/UUG0YUfUVseYOH44Qwf15MlnZpvw9WiItB+9qju8EKCrYgUs5tnI0nIUWJ7i5Wc/JBEvVdRl9ePbucu4+U/P8Jv/u5PfnnkfT059l3iJ8ejS8sIch9zvNA/SNVoX0gh026/pJ1MKO+4Sak4j/aCg6KIETrOL9DRWWhNZnKTu/Sai9Z2Tuilh8h+/xXBisRJE3ms5As1rDWLqQ29jWRLPU6SrOhPFdoVMi9CKVJY1XflcdLSmoEeYVI2NXSTBsWM/0tV4jiTZr8KUq+jUyDzwGVYEwI+4M76h/NF64TILpyVJ9QgTWt5ZK8gsWrXfpmkZFKZqUcrkdDmWYZUXmPBz10O7ikG9asvO67ti3Mj+3PdMXsE9IUjWWoQbPeykLrCuVVdFuPDcvVl/VGF12WN+8XdaWhLd+iFpKTrtyFdVEnPvXlUMG9qLb75dVvpREoKIY7HbLhtz8EFbMnBAHWcP+QmLljbzwecL/XpVOvt30w0HccZRO5a85sL5K3j84Rm89docUikXb0U7Imp8NF5dDK8qhLOkFRn3mT0y1F9FhHx3cuBcV9HU2E4kuuYX24fvfYO//fk5pCVMEIaAt9/4grv//jLX/PUoBucFsSyubyBV4xBpKF9w1q3sEGCT+eI8hfC0sbIkvQJ/sInU1Nm2HYNBNCBbk6jaaMF9LlYGqXffai67/ohuzH7tRCC81iA+mPVN1kRVwMuHX/qAbpgadKZcRF5LrYng0JbS6JAw9u1ygYPgb3X9kFwv7weiNSJTMjxbF8qPnMvw49nmfKG0MVN2EdWlpdHAnLbOC7UAnBaXxvUqjTmkvUMovgQVdhDaxfoO2kV3sN349ehdW8Hypvac6UwKkj1sUmmNnVA4bR5Om8sf/nQUo8YM6NTH1/NW+Atv15uPjmZRy5KMHjuoROvvBiEERx02ict//3jR45YUjF6/P3+9/qcFgqIiGuKmCw/l1Zlf8MTLH7F0RQt9e1ax9w4bs/VmI7BKmPKef+oD/nDJowWJwhKwUincmG0YT2yb9IBac9BVhBvKRLWVYuzIn6MUVFWX1gJXF9587XP+9meTMqB8s3nmcWloaOW80+/ljkdOwfYDqJavaKF1cIhQUxpRhP0+o3XFe+UJL20iArGNv1j5ARZe1FCkZSpN55604vcqq8UmXWRFCM/T/PyUKXz1yUKmv/I56bRLVXWUfQ7egkOP3QZZ4vtdFxAIrzWI/AdFplWB2VA7EjcisRPlHd2iKYHUJlxWV4Ry5helsRa24g6tQtmiuDaV10+2ykaGAdzV2WgvL9bBpxMTyKRnWDRCds6EKEV3qnV0CQGEmtKEklamlmXeQf/+RGxSq+Ba+bBti+tO348Tr3iAVD49l9bGDBrXhBpS2Jbk8X/P5MzzC4XXv/7zjs9aQm41K0UZpTS6QzqA5yn2OWTLVTafnXfckIX1jfzjzlexpMDL06KGDe3FlZccUFTDsS3Jjluuz45brt+t63w8ex7XXPTvTp9nerbbXZQtTbJ6x4MrCWkJJm83hljFqk/y7goP3v169j52hPI0S+qbeOPlz9huZxNBPGxYX4SCFRtUUPd5e8FvMdODZ2PMyL7Qttu8XDXpDt+RF7MN64yfoynACDmKa+0CkFozcetRHHToVowd13VU7rqIQHitQWw5aSRP/ec9PE8RanaNPygPiV4OFQuTnWoVZTQyrTzszNPvKkRzAmqj2GGHVDxFVFvE57Sg+kSRZSwWAkxUYPYCvhnC0yZoowhUSCK1NrlWQubOU9qUhS2nfYniUWT5iLVStkYTwBNPvs+BB21Rtp/vig2G9WWcqOSD5UtJVttgmcrFkYa0YTzACJm3Xvu807lffbM0+/+s4lCCMspqaEcmXXQshLAkWsNxJ+/MqA06a3PfB0cdPpkdtxvDf5+ezbfzlhOLhdlh29FM3HK970RlVApaay4/56HybQAr7uI6OfOedCwsx8JLlw7eKQUpBY5tcdRJO3znc78vUkmXj94vn9JgWZIZb36RFV6xWJgNojE+TiVoGFuF1e4RWWp8Y25UUj2nhVA78FULiYGG3d8qZ2H0mXEydF5A2R+KlIIddxnLWb87uNvzXBex7uqM6xDa25N88Xk9W0wemTVPWW0eVoaNwYd2JG0DwqQrZAcjlAbPw0orVNgy1YIxZjsr7bHZViMYMrovyrGIRBxCjWlj4y4RJKEsY7aQKYXdZvw7eADC5AcVgxDm2l5eYIUQWClFn8po6d+Sn3AqS1S/1YCwBFtvNrKswx7gm2+WlXSQfx9Ynia6NEXtl+3Uft5G9TdxQs2FxUHdImwmu0/ZOI9OSfj5VR3m4CnsZW1Y8bTxIcbTjN10KJf96UgOO367VT4XgEEDe/DzE3bgyksO5IKz92LrSaNWieAC+OyjBaxY1lq2jcBYFmxbZhOJB/etZZe9xpc9r6IyxKHHbI0TKgxsGTC4B9fccgzDRvb5PkNfKXhd8AwChuuyw+/mynMOIpRwQWm8mEXb0ChtQ6Mke4VI1Zh6ZKGmFFUfNxJZGC/v8xbC+ICze0Zd+neKiWQdP3G9bs1vXUagea1GtDTH+cctL/Ls07NJ+wUna3tU0NTQjgpLnFbDReZG80rWW4Z3ULlpk/ei/WdWCMNGIM2DLNpTxrzVnGDGo+8aSqnKMCpkG62sLQ1hK5s/A77gCgmUI7ASCpEfTUsmkMP8GEvVBNKOna0EWxGyOfWsPdhhl7Fc9vvHeeWNPO0k63zWOCsSheXoO2CLbdenqiaKZcssT2EplEqO/T7YcJPBzPlsUdaf0RHSEmy4SWff1NgNBpn7JnLmU2dRE4Qs8135hMXZeBcAT3Hyb3Zn+OjS9bnWZnw8e3632oVCFgP69+DrTxZitadZvLCFp2d+ix22cYuY32zH4jeXHcjk7UZzyDHb8M70L4i3pxgyvBcbbjL4e5MarywiEYeBg3uwcP6KkvJFKc2YDQvTCYYM7cP9VxzDuVc9xJx0KstuEku47HfY5sybuYgP3pmLbUnSRWneOsNsWk2ppVIFbKUU1NRVsP0qKsi5NiMQXqsJba0Jzvi/u5j3zTK0lwkUFjSuaMMLSZRjiGudNoXdprIlQERamfpVYTvrpC00Q4FsSxYkCwuAlIe9oh23LmZYHiI2dnsa4i7pXhEIWWihQUpTpLJIWH7W2etqE8ZbNNQeUJo9dx7LmRfum/340vP25bU35/DYf9/jgw/nk2pPGaaElpRh5ggbk5zG5HNlTKGDR/bm4qsO4eWXP+Hppz8oeT+lFIwe3Z9QMaaM74m9D9icqQ++VfK48jT7HVzcN3XKiTvw57+/hLIFVtwzG42Uh6/KFsWns75dZ4VXSUqmDqisDPPtW1/TkQXQS7pGsIcs8LV4bVl4IYuLz3uYE36xI4cdtTU77DJ2lY99ZSCE4IDDtuLP1z5Z4jiEIw47F8k1GzasLw/ceirLl7fw6SfzqK2tYKOxJk9Ra81H737Dq89+yH+e+4D2roSzzmn1bkTSp18PGj9flo1ezoylsibKlX87jlC4uPn/fwmB8FpNeOj+N5n39dJs3SUDUyhS2XnJvOBz9+UFZguJlTELdHioRTxVILjInYUG7KY46d6VRli1JrCExG5I4YUFbsiEocu0LqkJZQWYh4kqLDhojgoE05+czakz57LLQVvwkwM2JxILsd3k9dnOL0XS2NDGDVc9wZz35yGBMRsPRoUkH8yeRyqeprZXJQcfOZk99tkUgG23HU3Pni/S0FC81IVSmsMOm1julq80Bg3pyenn7MWfrn6iYDHIOOkPP2YbJpQwwxy8/5a4nuKW21/udlDC6tAe1xTGbzGiW+0a6puLM7EIU+AU8IMODDIa9z9ueZFRo/szYcvuXWdNYI/9NmP2u3N5+fmPEVJkf9OWJRFScNFVB1NRJpCkZ88qtt5mw4LPhBCMnTCMsROGER1Sxz/ve730APyIXyxBKmYR6xXjpptOpGlJC/998G3mfLSAUMRh6503ZMq+m1L5A0Rk/hAISqKUwcrS+2ut2WfKNSTbO/tnNJCuCXX6TFsY4aAU0vXJapUuLLynNLIl0eUama6NosM2sjWJ1ZbCdiSWJbErw7Q0tOP1Ll/+RIMJUXc6BHUAMmUip+wlzVhpDw0MHtGHa+79BbXfk+boq6+W8JuzHqCpqT3HaOULk+OO3Zajjtq67PlKZYTOygmHj2bP49/3v8nMt75EKc2GmwzmgMMmsuXkUd06/5lnP+BPp95TMiEUAAF3vHAufQf1WKkxrg0475S7eW/G1x02Zjn07lPFsiKEuuCzoWciVotASsGELUfwu+sOX2XjXRVQSvPisx/y2MNv8/WXSwiFbLbdaQP2P3QiQ4d/v2rELS0Jjv7FP1jRWITeRZt0FG0JImGHPXYeywlHbkN1mcTxHxJrsiTKahFeLS0tXHjhhTz66KMsWbKETTfdlBtuuIEttjCRYqXs19dccw1nnXUWAMOGDeObb74pOH7VVVdx7rnnZt/Pnj2bk08+mRkzZtC7d29OPfVUzj777IJzHn74YS688ELmzp3LqFGj+P3vf88ee+zRrXms7BfR2NDGwXsUr+fVUXgpW6BCpqVMKqyUn8WR97XIlIdIKYSrsLpgI9eAVxU25J1KIVMeVnMyR7wLpAfWls2pyeShZEOdM4LL9ZOWAXtxc5arT1qSzbcbzaU+S34GDQ1tPP/MByxe1ERVdYQdp4xl8NDyxQFbWxM888wHvPra5yQSaUaN7Ms++2zKqA7Jwfm484ZnefyeN2jz2bGraqLsf9w2HP7Lnctea3Xgb1c/waO3v1qUUURagsk/GcsFfz5qjY9rVaKpsZ1z/u8uvp6zuNOxTbccgXRd3p3+ZdFzvahjzIZlnr9QyOa/L55b8vj/IpYua+Giq6bysV/FOoP1hvXm95ceRM8elT8oS353sSaF12oxG5544ol8+OGH3H333QwYMIB77rmHKVOm8PHHHzNw4EAWLSr8gp566ilOOOEEDjzwwILPL7vsMn72s59l31dV5TSG5uZmdtllF6ZMmcItt9zCBx98wPHHH09tbS0nnXQSAG+88QaHH344V111FXvttRf33Xcf++23H++++y5jx64+m/rHH8wvW95DeAotBcqRKJ+dQiYVdipvwctPYA7bSO0WzZIvBi9q4UUswELFHNy6KKmES3RuI9JViEQaFXVKcgmaIIS89ykPuyWFjjimXLunEHkhz8pTvP3SJ9TPW0G/wUajeOSBN/n7TS+glMayjPnt7ttfZZc9NuFX5+yZTejsiMrKCAceuAUHHlgYEr9ofgP1CxuorI4ycnS/7AboN0fewkfvzC1o29IU564/TeOjmd9wxd+P79Y9W1U49szdWPD1Ut564ROkJVGeypof1994MGf87qA1Op7VgZraGDfecSKvvfAJzz/5Po0N7QwYVMdu+23GZluO4LoLO+eABSiP3r2quPm6o5i/YAUffLIApTQTNx9Orx5dV2X/sWKVa17xeJyqqioee+wx9txzz+znEyZMYPfdd+eKK67odM5+++1HS0sLzz//fPazYcOGccYZZ3DGGWcUvc7NN9/MBRdcQH19PaGQ0WTOPfdcpk6dyqeffgrAoYceSltbG0888UT2vK222orx48dzyy23dDmXld1FvPjsh1x18aNFj2nA86MAteVHF1omcKPsvkprrJYUVmvShFyXaga0j6gxppkO54ukR+yLFaiQhde7Khty37EdSmM1xcFn6hBK+6zxEq8mitXYbsbRAWf94TB22nczpj01m2uu+E/R8QkBe+03gdN+s3u52Wbx9ReLuenap5j9bk4L7z+ojuNP3pmGRY3cckVxRokMzrz6IH6yf9cFKVcllFK89/ocnnl4Bovnr6BHn2p23m8Ck6ZsiFVCaP8vYdH85Ry3x/VFj3VpNrQEm20+gqv+uHaZDYth/jfLeWf6F6TTHqM26M+4CcN+sKjItQXrtOblui6e5xGJFBYGjEajvPbaa53aL168mP/+97/ceeednY5dffXVXH755QwZMoQjjjiCX/3qV1l29unTp7PddttlBRfArrvuyu9//3saGhqoq6tj+vTpnHnmmQV97rrrrkydOrXo2JPJJMlkblFubm7u9rzzMaiEaUxrjRezjfaiNcIzZQpkuWrJGfiVfFXYNjlDxfoH3OpQZ8GVOT9i41WGsFtTsKIdt2dFYX6JEKA0drMf2p4XOi4A4Sp0SxxZhr1cKc2df3+55HGt4b+PvcuRx25Dz17ld5Vzv1zCr46/nWSycL6L5jdw5XmPUN2NdeLevzzPYksx8+N5aK0ZP2YQ++y4MT1qSxeo/L6QUjJh29FM2Hb0arvG2oz+g3qyyebDmN1BIwYg7bO0lKCDUp7mwENLs458/cVinnx0JnO/XEqsIsQ2O23I9lM2IhRec7Fnba1Jrr34Uaa/8pkJWxfmuR84uAcXXH0w661f2sQdYNVhlYc9VVVVMWnSJC6//HIWLlyI53ncc889TJ8+vZO5EODOO++kqqqKAw44oODz0047jQceeIAXX3yRn//85/zud78r8GfV19fTt2/fgnMy7+vr68u2yRzviKuuuoqamprsa/DglaNVGbl+P0aM6lu4C/MUXsSU7gbj9xOQfXUHWgq8ihDpmnBntm7ArQqR7FcmaELrbBl7K54iXSEKyEBlSwK7MV626KTVnCw6XiEEG285gq++WMzi+qby89CaN17tzFjREX+74VmSyXTR6EOA1qZ4l33UL2jgpgde5c335/LW7G+47eE32P+0v/HGe8VLzAdYNbj678czfqvOEYNCAymf1ih/4+T///if78jmJSI77/nbS/zi8Fv4779mMnvmXN569XP+cMlUTjrsJhYvalzFMygOpTQX/uq+LOOK1jr7fC5a0MBZP7+T+oVrZiw/dqyWmN27774brTUDBw4kHA5z4403cvjhhxeNArv99ts58sgjO2lqZ555JjvssAObbLIJv/jFL7juuuv485//XKAZrWqcd955NDU1ZV/z5q1cpVshBL++YG9CYds4WZU2zBTh0uaS7sCL2XgVDm5tlFSfSlRlGBVxDPGtY5EcWNUlSa6KOqT615DqU4XTnDLciEL4LBjly7cbFA9E2HrXsfTuX9utkhVCiC7bLV/awjtvfllScBUfSYl2BWukJp32OPePjzGvvqGbPQT4rpBScvVtx3P3tLPY85At6T2wzvAdRh2EY+e+vAw1GVBdHeHwo4tHlL7w9AfcfZvR6LPk1n4fSxY1cuEZ95V9VlYVZr75JR/N+rZkOkc8nuTR+6av9nEEWE3Ca7311uPll1+mtbWVefPm8fbbb5NOpxkxonAn9uqrr/LZZ59x4okndtnnxIkTcV2XuXPnAtCvXz8WLy6Mdsq879evX9k2meMdEQ6Hqa6uLnitLEaN7s+Nfz+erbZdH+EpU4ywjHsxX4Pq1jEpULEQqjqCVxdDVUUMZUwXLkyhMdFejoWQOVNLUVNj/uWkoKIihMlz9vPT/L/rbTCQ0688iFkzvubm657ucgxKaYYM61XyuOt6vDTtoy6lk/DzpbywRbp3BeneVebVp4p0XRRPgFfReV7aH8O/np1V/gIBvjd6963h1N/uwz8eOwMZyxFJC8yzmP86/9IDivahtebBO14rue/zPM03Xy3lvRmrX5t+8ZkPkGUStZWnee7J2at9HAFWM7dhRUUF/fv3p6GhgWeeeYZ999234Pg//vEPJkyYwLhx47rsa9asWUgp6dPH8JtNmjSJV155hXQ65w+ZNm0ao0ePpq6uLtsmPwgk02bSpM4FBVcHhq/Xh0uuPoThQ3uV12h8lnbovF5nN6j5jBc++7v5rzYM81EL6ZZZ7TPh7plkaL9YZBa+MCw+PIFlW1x563Gcd8ORjJ80kkEjerPJxBH8+ppD2f/47Tj7mL9x7jF/46uZcxGJNJTghBNC0LNXZUnT0EvPfsgRe/yR265/pvRcMlOK2LgVIVR1DJA5E6wGbBuvdxXt/Yv7tjylee3d4uHcAVY9QiGLfz50MuFIZ+YHIeDXF+xVMjG5qaGduV8uKbsnsizJO298saqGWxItTfGSNGIZtLUmi6ZKBFi1WC1ezmeeeQatNaNHj+aLL77grLPOYsyYMRx3XC4PqLm5mYcffpjrrruu0/nTp0/nrbfeYscdd6Sqqorp06fzq1/9ip/+9KdZwXTEEUdw6aWXcsIJJ3DOOefw4YcfcsMNN3D99bkop9NPP53tt9+e6667jj333JMHHniAd955h9tuu211TLskXNdDuKq8yVAY6iQk4OXX7eksuGRbGivh4lZa4PhFG4Upg6ItbRKeKTwHwGr3ytb5UlEHLcBqTxW0GzqyD2dcvB9jNhnMmE0Gs90eZrORSrlcdvJdzHxtjrmcfykZT0PSxasKg5WLrpPSVCM+56J9i7JMvPLcR1x1wb9KD7DjLQs7hjOuA+NI9v8anCQkS8SFFCPbDbD60H9AHY+/eC5vvPIpD987nXTKY+LWozjqxO3LnteR9LYoRDfbfU/0G1hbwMJSDL36VP3oow7XBFaL8GpqauK8885j/vz59OjRgwMPPJArr7wSx8ntuh544AG01hx+eOeQ2HA4zAMPPMAll1xCMplk+PDh/OpXvyqIHKypqeHZZ5/l5JNPZsKECfTq1YuLLroom+MFMHnyZO677z5++9vfcv755zNq1CimTp26WnO8iqFH32q+nbvMMC/4gqYTMtFXvqBR+ZEcGmTSNQnCnpdLOJYU9CcAK2F4ElVGqGmNSGuspEJ2sWME0BEHN2wbYas0+x64Baf+ereibe++YRrvvl64280OWWms1iRedTQ7r3GbDeOEX+zE6CJlQDxPcdufnu1yfPlYb5NBfPbRwrI5dXZckVSqoOw8mOKMY9dfteVIAnQPk7cbw+TtxnS7fW2PCnr0qizLZu+5itEdyHFXB3bbdzP+89CMkseFFOx54JpNzfixIqCHKoNVlbNw520vcf9fn0eFLNJ1fmBKx5pPGWte9iNtAijSKmcOzA+pT7mke0ULNJuOUK5HaFk7Xq+uaZsyD4FtSb8UPBx2+CSOO367orvIRHuKw7e9kkQRCqx8eNGQ8bEBf7nzZ6y/YXGB8cG73/Cbn9/R5TgBnJDFocdsw5vvzeWzDxZ0GWTS1sdBhzvfp1suOYxxo1f/ghfg++OBf77KHTe/WNQcJ6SgsirCff89c42EzP/12qf4z0Nvd/pcSsGQ4b25/h/H/yBFM9cGrNN5XgE6Y4vJI7nn9lew4mns5XFUhWMCOIQwbBWuQjtWJ9OXzBdcUCjwwg4yrVGlZJfWyEypjq5KrGuNV2Nz7P5boVxNXV2MHXbckB49SudCff15fZeCyxD8qqzwKveDbmwowutWBLvvvxknnvoTKqsivHPa3d06x7JktuZspsLwSQdvHQiudQgHHTWZD2d9y4w3vsg3UBjOTlty8bWHrrFcr//7zW70G1DLQ3e+nn1uHcdi5z024cTTfvKjFVxrGoHwWgPYYKOBDBndj3lfLkHE09hNSXRTEhWShmE7bBdluhCuLit4ZMIz5sF8/5HWiJRCpn3/ViwMrmcYvEuYK21LMu3Rs74bd9p3UNiFgKEj+jBwSGky2l59urdL2+EnY6msMtrrlJ+M5eN3vy09RMByJFtsPpz3Pp6PBsaPGchhe2zOVuOGdXv8AX542LbFpdcdznNPvs9jD73NvLnLiEQctt9lLPsfNpGBQ8pzZq5KCCE48MhJ7Hvolnw9ZzGplMfQEb2zz2WANYNAeK0BCCE45/y9OfO0e0iHLDxPk45IZImy90Zj8U2EZTQmAVhtabzKkMnv0hoZdxEdfFumPpcGSxT61vy+b/vHid+Z9HP46P5EYqGy2ldG6dOJNFtNHFHWiT1m7MAui/717lvNxpsNzb7fc89x3PbX54i3poonTgM/2WMTfnPWnkWOBljXYNmSXffZlF39Mjo/NGzbYlQR/22ANYN1t7DQOob1R/fnr7cex3Y7bGDqACWUiSKkMDw+8/8+fWu617Ef3SfSHjLhZoVe/mIuyTDTuyYXzH+lw3DgoZsztEzOVSlEYiH2OmyrsgJJK4VsbEE2tvLQ9U+x7/pnMfX24tRRQghOPnsPkzAtOh4zr/87a/eCKEUpJTfcdAxOxM7cioK/YycM5cxfd49DMUCAAOsWgoCNMlhdzsdkMk1raxLLsvj56XeweFFTLjTdEuyx+yYcsNum/N9Pb+2yLy0pTrBbrC2gJMT7OHgVDjKl2XXkMM69eD8iEQfXdfnL9c8w75tljBrTnxN/sVOWS7IYUimXS//vTt59/Yssc3r2WkpBIlk0NP/E3+7LgSftWLTPmW99yc1/eJp5c5dlx1zXu5I+vatY+OUSlKcZPXYQ+x05iYnbjzZsHYkUd975Gi89/zGpZJpefao55rjtmNzNOlwBAgRYNVjn63n9r2BNfhEdobXm/468la+K1EyCPG0tPwesG1C2oGV4BTKpiNUnsRFstfUoWprb+Ojtbzq1nzhlDJdddWjJ/jzX4/VpH/HUQ2+z8Nvl1NRV8MWsuegMf10R2I7FY3OuLVk0UmvNx7Pn8dBDb/PWjK9Iu375FU8h42nslIdWmgOOmszPfrN7kFMTIMBagkB4rSX4IYUXwKcfzueME25HdSiB0h3BpensT9MY4eVFbex4rgSLUh6Wz7xR7DoTdx7N5Vcf1q0xP/bPV7jl4q7rOZ1xzWHsethWRY8t+GY5v/nNfSxZ3iGvx/fTiXgau8342i6+4Ugm7bhBt8YWIECA1Ys1uWYGPq+1GGPGDuKMi/YpfrALjSufoSP/MyEETp7g0l5xwZX//u3nP8N1S2tS+fh2TnHG/o6Y+1nnCgMATz08g+MP+jNLlrV0PujPV0cdlC2RUjD13je6db0AAQL8byGINlzLsdue43nwgTeZ//ninHDJLOKUL6fSSYsSgCo8oEX3+vjrDc9y+q/36HK8vfp3L9Ck94DaTp/NevNLbrhkKm6PaPmTtUZFbFRrik9nz+90ONGe4qXH3+Oz979F2pLNtlmfrXYyhSA/+3A+77w+Bzftsf5GA9lyu9FFqaoCBAiwdiMQXusAbv7b8fzyxH8wf84Sn3hWo7vh59G++iXw+RGlpGNoRzmuw3x8UUJT6oh9j9ueu//wVNk0MCEFex3VufTFg39/2TB2W7JrHkhLomWOWT6DWdPncOnP7yDRnjKs9xqevO9NIrEQtf1rWbSwEWlbJj/cVfTqW82F1x/B6LGDujW/AAECrB0ItpzrACKREP+855f0iYURCReRdHGjsqzGpIFUjYMSPk8iopPgArLh+l2h38DSCcb5iFVGmHJw6Uq4ALsfMYlQxDDYL29q45apr7PX2X/jOXcFyzesNkK3DDTgVtk0btyD5tE1/Pv591FK8/5bX3L+0X8j7ueeaaWzdEKJ9hT1Xy6BZBrleng+Ke+KpS2ce+Lt1M9f0a35BQgQYO1AoHmtQ6itjbHCr1LsRaJoW+K0uEUDM7QlSNU4RBa2gQBVEe7E1iEECFXe/Jg59utz9u72OM/8wxG4aY8XH51ZeEDATw7eklN/dwgAcxet4GfXPEhTW8KE2dsSr0KQqtWEmlTJMQnAjRperDbP5Zq/T+PRf77G0vfmF/I/lppQyjWl6IUJ708mXR69Zzq/PDdIZg4QYF1BoHmtQ9hu902yBSCdxiSJPmGSPUKmlIrS4JmXFpDo6WC1pUEY06BsS0LaK6B1Gjq8Nxf/7hD6Dqkrer2M4BoxdgCRyHfb55x9w1E8OPtK9v/Zjmy9xzgO/PmOPPzB7zjzD0eYvrXmrJv+Q3NGcGUgBMka2+SvFalGqdF4tsCNmkfXanOp+aSJJR/UQ7KzIO8IobUxlebdB+UpXvzvrO80vwABAvywCDSvdQi7HbwF//7nq7Q2x3EaU8iER7rSItQospRQApCupmJREqVNWRMtBEKDlUijE2mQgnOuOpgdd9sYIQTb7jCGIw+4gaXzGrNRihkhMGRMX27+589WarzVtRWcdOG+RY/N/Gw+Xy8qbqrTtqCtv0OsPo3lkRM0QqAcQaKnY0LmXUXVVy3Gr6c1SIl2JAjp10YTZi4aRCJt2vjzw9MFW7f2LkiGAwQIsHYhEF7rEGrqKrj6zp9x0c/vYFl9E1VfNePFQsh0oaks83+JMEEaWmcFksCYH7f/yUYFyb33/vt0FtU3cuWFj9CwrI0+/Ws4/ezdefu1L7n8/EewHYstJq3HdjtuuErYuz/8alEnVo58qLCkdUiImo9bsJVAaU2iZ5hUrxxjd2hFMie4ACKOCWaxLUN4LKV5D1AdRbQnkS2JzhcT0H9Q93x6AQIEWDsQCK91DMPX78c/nz2LN1/8hCceeov33u8cKp6FEGBb6FS6QFAdc8oUPE+RSKSJVYSzx/r3q+UvfzsRgFdf/ISTj/6HX21YI4TgxWc/5PabXuDqG49kyLDe32se0o8ELAtfuxIuUBtGCkG4IY0KSdJR4+/rCG1JVCxUeA8yx2JhPCmxUm6uOKUv+HYLCggGCLBOIfB5rYOwHYttdhmLcOyuS5NIUbCAh8I2Lz77IXtvdQUHbHsVR+95PY/c9TrpdE4QzPlsEVf+9l+kXQ+tNVqT1ZBWLG/lnFPvJZFIf685TNxwKKqLsQtXgeOQ6hnFsyRWSmMlFE6LR2xJGi07x0+qsF+tu1iovRAQcVC2hRbCz30z+ugrz35I8nvOKUCAAGsOgfBah6E89Z3PSSVdZr8zN/t+yaIm/nb9s5x5/O2kfT7CR+5702hjRWSLUprly1p4adpHKztsAEYP6cOE0YOwSpVi0ZrIMhcV840DPrW8yCMh1hEHlV/KTAB2FzliAI5fwVPKrBD7/MMFPP7gW99jRgECBFiTCITXOozNJ4/qeqHWukA705j8p4747MMFHHHAjXz88QKmv/oZXhnBKITgzdc+X9lhZ3HVz/diWD/ja5L+PDKzCTW6hJq9zAU7j8H/XMWc/IF1i6RY57fLsJVozeMPBMIrQIB1BYHPax3GgUdP5o5bXjB+qRJVknG9zqzrJRb4pvpmzvzNfYiUl/tQKROpl0ib/qREhx3aW+MANDe188xj7/Hacx+TiKdYb3Q/9jp4SzYcN7jL8feojnH3hUfywrtzePLNT2hsifPVrAU4K1LYcY3n53KVggC8qIPV4kcKqvKVp7Pn5Qtvv73WmsULG/FcD8suf90AAQL88AhY5cvgh2aV7w7efvUzLvzV/eZNZtHOLOCu10l4aVnerOb1jlKJJN6aQrsusiluhAKFZL9WyOayvx3H7y/4N81N8SyThWVJPE9xyLHbcPxpU75zuZLdJ16GZxiETSJyV7yDWmM1JrB8getFHGMWLHVdrRHt6c75YEoRCtn85+2LghIrAQKsJAJW+QDdxpbbjqbPsJ7GFKiUEVxKQypthFceiW9XnIEa49NqSblorU1YuSqszJz5v0q5XHjSP2lpyQkuIGtufOiO17j24kdpLRaaXgaxSM4MKDzddUCKEAjHRvt+LJF0fdtoh/P89yLlFU1kllKw/a5jA8EVIMA6gkB4/Q/ADtnosI0OmUVch2102DEUSI6FCvvHy/BPaECHjHDTtmD0+v1MmHqZ66qki0qX9o09/8T7HL7XH5n+6mfdnsuJp03xB6SRpcyheRg/bgi1dTF0yEZFHIQUiOY2SKQKBZg2gk24RcarNZYUHHTMNt0eZ4AAAX5YBMLrfwAbblyEEd2SRpiFbLRtISMOA4f0LKpZZJZ45VNACSkZ1K/r0iaG4b5MxKOGVDLNZec+zJefd6/O1+4HbkGPCpOILDyNTPr+N93BT6U1joYV36ygsaHdfG5JtC2QTe1YS5qwFixHLm5AtCYR7amSgktKweU3Hc2wUX27NcYAAQL88AiE1/8ATvrlzpSIbM9i25035Opbj2HAYBPdp/NeGVOg1ZbGSrpM2mo9Innmu++DjMx5+N7p3T7nvmlnMXJIT/AUMuUh466hc8qLnKxxHHRbmkULGgpPtix0NGTm5ClkIo1sau3EZwimr1DE4e6nf82mE9f7XvMMECDAmkUQsFEG60LARgZT//0ON137VAEvYeb/fQfV8s/7/w/btvA8xbVX/ofn/zML6emi7bfaYQwHHzWJ3xx6U5dlV3RFpKhpL1v80jGmyHDE4fGXzvtOc0qlXJ7/z3s0NLSx3S4bIRybtrYEM9/4kjtufbG4O8z/UC5ckaONwgSq6GgYwj4voiXZbsqGnH7hPlRURr7TuAIECFAca3LNDELl/0ew3wGbM2K9Ptxy47N8+Vk9WmkqqiLstvd4TvzFTth++LdlSfY/cHNefPQ9gKKciG++9Ck/2WscPQfUsmJhY9HraUCEHYQli/ITCkxZloxgyyRAfxeEQja7H7RF4XW15opzHymtZQph+AwrwojWXLCIUArRFke3xSES4td/+ik/2XPcdx5TgAAB1g4Ewut/CJuMG8JN/zixy3ZP/mtmNqS9GKQU/OfBt/nDvb/gF3tdT6ot2alNZV0F51x/BFee/RCJeCqrBWW0N1O52U88Fqb8yvdBa2uSY/a9ntblbdnq0EhhogyLhdM7xR9tAcRqYmy384bfazwBAgT4YRH4vH6E+OLTRWUZNJTSzPl0Iddd/V8SlWG8ygjathC2RVWvSg765U7c99oFbLH1KG771yns4WtHGVOhtmUBTZPWsG8X1ZXLoaGhlQO2u5K2ZTnBJcCE8SddU6fsO+CSm48hvIp8egECBPhhEAivHyGi0VDp0smAktDqKd5/7xsUAl0RxutRQbpHBU2OjZaCphVtAPTpV8Np5+/FyRfsBWELGbEhYy70/0zabjS77jV+pcd71F7XF9QryyCbe5b2TI5b9oBAJovX5zrm7D3ZeIsRKz2WAAECrB0IhNePENvsvGH5isPFmNl9PkDlKR588G2O3u0P3HHjtGyC8j4HbcHv/nQEY8cPyZ7Sr38tvzhjVy763cFY9so9aqlUinRb6UKR2RG6fqCGJek/sI59D5tEpCJX+2vwyD6cdeNRHHbqLis1jgABAqxdCHxeP0L8ZO/x3P+PV2huakd5GWdVploxyEQabQm0Y2f9VlkIARI8rXng7y9TURXh4OO2BWDzrUay+VYjSSVdXNcjGgt9b8aK1178xIS5dwGhFBqLvv1q+P2ff0q//rUcd+5eLFnQQChs02dQj4A9I0CA/yEEmtePEBVVEa657Vh69KoCTICG0Nq8lAZPIVIesi2JKBYl6Be5BHjg7y+TShbWwQqF7YIil98HscpQ1438MV1w+QH8/f5f0q9/LQDhaIjBI/vSd3Dx5OwAAQKsuwiE148UQ9frw52Pn8F5Vx9ELOoUWgjzXjLpGo7EjvA1tbaWBLNnfL3axrnV1hugu0jAFkCsNsL2UzbCcQJG+AABfgwIhNePGLZjEQ7btLUkSvLfakB21L60hjyqpXh7aZ/UqoDnyJI+Oo0pQlk3sHa1jiFAgABrFwLh9SPH2698jlWm7IihWcpjd/cFV/4Zg79nDlc5pJIuIhzCc8wVMzJW5/0/HZK0tq5eARogQIC1C0HAxo8c6e7mSGVqhCmNiBtBIaVg/bEDVyuhre1IUIbj0LMEQkrwDNu8sgQinsaW0PbVUo7Z6WrW22AAx56xK0P8Ma1Y3sqTj7/H29O/xHMVG20yiL3224whQ3uttjEHCBBg9WO1aF4tLS2cccYZDB06lGg0yuTJk5kxY0b2+LHHHosQouC12267FfSxYsUKjjzySKqrq6mtreWEE06gtbW1oM3s2bPZdtttiUQiDB48mGuuuabTWB5++GHGjBlDJBJh44035sknn1wdU15nMWqDAShVhhkeY5bLCC3RlkQC0hJEoiHOuHj/1To+KSWVUiI8jZVSyISLSJuAEqs9jUy5WC0JvLYkS75dzhvPfMDPd/8DN102lVkz53L0oTdx9+2v8slHC/j8s0U89u93OPGnt/Lk4++tsjEuqW/iztte4rLzH+aayx7jjVc+wyvGYB8gQIBVhtWieZ144ol8+OGH3H333QwYMIB77rmHKVOm8PHHHzNw4EAAdtttN/75z39mzwmHwwV9HHnkkSxatIhp06aRTqc57rjjOOmkk7jvvvsAQwC5yy67MGXKFG655RY++OADjj/+eGpraznppJMAeOONNzj88MO56qqr2GuvvbjvvvvYb7/9ePfddxk7duzqmPo6h533GsftNzxLMpkuGhUhBBx87DbUz1vB689/jMLwI267y1h++sudGDRs9WowqZRLorG9gEA4i7SbKzKZGa//9/G7Xue//32ftG1DPIn0C3OqsKm0fP3vn2T4iD5ssNHA7zW+xx6ewU1/esZQYmmNkILnnprN0BG9ufqGI+npR3QGCBBg1WKVs8rH43Gqqqp47LHH2HPPPbOfT5gwgd13350rrriCY489lsbGRqZOnVq0j08++YQNN9yQGTNmsPnmmwPw9NNPs8ceezB//nwGDBjAzTffzAUXXEB9fT2hkAmnPvfcc5k6dSqffvopAIceeihtbW088cQT2b632morxo8fzy233NLlXNYlVvnvg+kvfsLlv34AQa4Sss9vy5bbrs9F1x+B7Vi0tyVpbmynpi5GNBYu32kHKKV45/mPeOb+6SyZv5wefWqYcshWTNp9HHaZCMEXnpjFNec8XKxDRLo02W+W1T77xkAAyrERvSrZZscN+O1lB3yneeRj+qufc/HZDxY9ZlmCYSP6cNOdPwvC9AP8aLAm18xVbjZ0XRfP84hECstMRKNRXnvttez7l156iT59+jB69Gh++ctfsnz58uyx6dOnU1tbmxVcAFOmTEFKyVtvvZVts91222UFF8Cuu+7KZ599RkNDQ7bNlClTCsax6667Mn168dpSyWSS5ubmgtePAZN23IAb7v052+6yEU7IRggThHHKBXtz8Z+OyAqXWEWYfgPrvrPgSiXTXHLUzVx81M28+cxsvpg9j3de+IjfnfR3frPvdbQ1x0ue297amRQYAKW7DJ8XflRHfug/gEi76GUtvPPWl99pHh1x/52vITomcfvwPM2XcxYz65253+saAQIEKI5Vbjasqqpi0qRJXH755WywwQb07duX+++/n+nTpzNy5EjAmAwPOOAAhg8fzpdffsn555/P7rvvzvTp07Esi/r6evr06VM4UNumR48e1Nebirz19fUMHz68oE3fvn2zx+rq6qivr89+lt8m00dHXHXVVVx66aWr5D6saxi1wQDOvfoQwDd/rUJt4Z9XPsY7L34MgPI1u0wZlTnvf8sNv76X8/9WnA1/3MTvx0NYbBYCw4foxVc+QrG1JcGnHy0o28ayJG++PodNtxhetl2AAAG+O1ZLwMbdd9+N1pqBAwcSDoe58cYbOfzww5HSXO6www5jn332YeONN2a//fbjiSeeYMaMGbz00kurYzjdxnnnnUdTU1P2NW/evB90PD8UVqXgamuO8+Rdr6Lzan5pAToWQdVV41ZX8sqLn/L57OL3evDw3vQZUFtskOX5GbuABtzlbVx06j0s/HZ5l+07Il3GZJmFWLk6ZgECBOgaq0V4rbfeerz88su0trYyb9483n77bdLpNCNGFN9Fjxgxgl69evHFF18A0K9fP5YsWVLQxnVdVqxYQb9+/bJtFi9eXNAm876rNpnjHREOh6muri54Bfh++PTdr0klcvRR2rbQvXuiqyshHIJwCF0Z4/Tj/sFLz3xYtI/Lbz66cy6aIwtyvVYGWineev1zjt//Rl577qPvdG51TYy6HhVl23iuYr31iz9rAQIE+H5YrUnKFRUV9O/fn4aGBp555hn23Xffou3mz5/P8uXL6d+/PwCTJk2isbGRmTNnZtu88MILKKWYOHFits0rr7xCOp1bGKdNm8bo0aOpq6vLtnn++ecLrjVt2jQmTZq0SucZoDSyxL+AFgLdo9aQ/YrCl1Ka35//CHM+Xtipj6Ej+3Lb46czeuNB5oPM+RHj78wXYBmB1h3lUVum5pjWmivPfYj2IkU3S8GyJPsctEVJLVUIU3pmp12CqNYAAVYHVovweuaZZ3j66af5+uuvmTZtGjvuuCNjxozhuOOOo7W1lbPOOos333yTuXPn8vzzz7PvvvsycuRIdt11VwA22GADdtttN372s5/x9ttv8/rrr3PKKadw2GGHMWDAAACOOOIIQqEQJ5xwAh999BEPPvggN9xwA2eeeWZ2HKeffjpPP/001113HZ9++imXXHIJ77zzDqeccsrqmHaAIhi1yRBkRmuKhnOCpwiEEPzrnjeKHhs4tBc3PPBLHnrjfOyQZfqwJERDYMmcABOAYzN+m9HIEsEU2baZastCoDTc8efnvtPcDj5iEhuPH2Kmk3cpy5JIKTn/8gOIxrpJLBwgQIDvhNUivJqamjj55JMZM2YMRx99NNtssw3PPPMMjuNgWRazZ89mn332Yf311+eEE05gwoQJvPrqqwW5Xvfeey9jxoxh5513Zo899mCbbbbhtttuyx6vqanh2Wef5euvv2bChAn8+te/5qKLLsrmeAFMnjyZ++67j9tuu41x48bxyCOPMHXq1CDHaw2itncV2++3OdKS6HD5hdzzFG+98nnZNk0N7bj5CcBSGg0sFkbHwhCLYMdCDB7Tn8EjOzN/ZPLFdDRUWO5Fa979jtGHobDN7/50BCed+pMsk71tS7bdcQNu/MfxTNx61HfqL0CAAN3HKs/z+l/CjyXPa3Wjtamds/e/ni/rW42fqwxCYZvH37yw5PGG5a0cvtPvy/YhpeDok3fGsyR3/fEpRCKdrQmmLYn2E5ULoDWDhvTkH1NP79ac8hFvT/Hc07N5/ukPaGmOM2hIT/bYdzO2mDSypPYXIMD/ItbkmhlwGwZY7aisifHHJ87i0lPv5t13vy3ZTkrBqA0HlO2rrmclYzcbysezvs2G23eEUpoGPB6+93WsiggiGoK0B4iSeVkIwUbjh3Z3SlksXdLMb355J4sWNmYTuxfMX8H0Vz9nu5024LxLD1jpKtIBAgQojeBXFWCNIBILcfplB5TVRJTS7Hf4Vl32ddT/7VzIoJEHIQQbbDGM+56Yicr41qQExyotuHzjw4m/2qXLaxeeprnsvIdZsrg5v5tskMqrL37C/Xe9Vur0AAECfA8EwivAGkO/gXWcceE+ICgIfc8ItD0OnMC2Uzbssp9xWwznwusOo7LSsLhYtkRIgRDwk303ZT4mt8qL5JkGhTAEw3SOTkQITjt3T6prYgAsrW/iwb+9zE1XPs79t75I/YKGouP49OOFfPbxwiylVkdoDY8++Hb3mfsDBAjQbQRmwwBrFLvutxmDhvXiX3e/wYzX5+B5ilEbDGD/I7Zi+13HdjtBevJOG7L51qN444VPmP/NMmIVEbbeeUPa0mkePf0O08gSuDELu90z/VrSaEdKZbWkocN68euL92PMJoPRWvOH8x/h+f/MKrjWnTc+x35HTeaks3fPJtoDvD9zLlKKkuZLgJbmON9+vTTI9woQYBUjEF4B1jg2Gj+EjcYP+d79hMIOO+y+SfZ9Mpnm24/m52qPAW6VA0Jgt7l+ThkIaRGOOpx/6QFM2m40AF99Xs95P7udpoZ2tOULUKVAgdCaqXe9TkVlmKNOyXFldjfWKYiJChBg1SMQXgHWeXzy8QLuuecN3nrzC7QGR4AKCbywSUJ2qxzcChsr4SG0RlmSCy4+gEmT1gfg84/mc+bRfzNUTpaVy0OzLFP4MpVGuB73/Xkam00ayUYThgEwdtyQsloXGDLjIcNWX6XpAAF+rAh8XgHWaUyfPofTT7ubt9/6MmsKFBpkUhNqTBNa1EpoaTvCVXgxG1UZoq5/NVtvORKtNf+9bzq/OvLWnODqCCnQkZAJsVeac468hS8+mg/A2HGDGb5en1wSdgcIIdjnwM0JhYM9YoAAqxqB8AqwziKRSHPVlY+jlO6kAQkAIZBa4KxIUPF1E5Gl7ViW4OIz98K2JA/d+iJ/vuRRPKVzgqujz81/r8NGgKXCNuf8310sXNCAEIKLrz6YurqKAl9dJgBlwsQR/PSE7VbX9AME+FEj2BIGWGfxysuf0laOj1AIVDSE1WLa2CsS7Lf3Zmy68RBWLG3hrj89a6IQ/bbl+kEYVg6vMkyjFBz101uYuNV6nH3OXtx6z895+vH3eO6p2bQ0Jxg4uAd77rcZ2+20YZDjFSDAakIgvAKss/j666XYtiyki+oI6XMg+uHsL/77XX72q9148T/vmkCK7sZSaI3QGrspjlsTBVsy4+2v+NUZ93DTzcdyyE8nc8hPJ3//SQUIEKBbCLaFAdZZRCJOlwETQC57GGhrSfDRu99QP68BbVvQXX+UEKiIjRcLIf0SL0pp5n27nOemFS/lEiBAgNWHQHgFWGex8UYDywsvrRFpD9GhTaI9yTtvfYnOZ7jXukDIdeoKUFEHHbFRFSHI6/PJJ9//PtMIECDASiAwGwZYJ/H+9DlcdsI/ENVRdMQp7rMSAqu1s09sxfJWFi1oyJ4jMIUpsayCHLF8qIhd+HmezFtc38Ql5zzIezO+RmvN2HFDOOCwiWy+1chVMdUAAQIUQcAqXwYBq/zaiRVLmzl++ytJJVwU4PapNiVOOjzKVnMCK54rVoqAzSaNxIo6vPPanE5am84rjpl/REVsdNgqFF7+tUTKRcZdpFKm2KYtkSELpeDok3bgp8cH0YYBfjxYk2tmYDYMsM7h6fvfJJ100ZkgisVN2IsakM1xRFsS2ZbCaowjlEbbMltdWWs4+Oc7sGRhY1Fzo9DaZ9VQaFugojZeTRjdUeuCrJCzWlMIT/l2RY1Meei2NGjNXbe9xOz3vlkTtyRAgB8dAuEVYJ3DjJc+KRA+ApBJF6s5juWBRCBsC2zLJBhXhNGOBdEQX32xhB69q0syzAsA7Qu9kFU+hN4/QeT+a6A1IuViWZL/PDzj+002QIAARREIrwDrHFQRFnctQNdU5KojC1EoeEI22pKkky4/2WdTdBdRiqJc+D0YNS6jcXU8F8DTeGmPj2aXrl8WIECAlUcgvAKsc9hoixGdKZnCoc4CK4O8iMIRo/uxzS4bsf5GA5FW57YZWZQVXmVcwjKRLlZSzJwPoDSWXYRyKkCAAN8bgfAKsM5hzyMnd2Jq12Gn/Em+ALvu4qmc9tPbOOIXOzJpxw2KWwWlREiJzAR75F/LD6kXaQ+ZcMteUkqYtM36XU0nQIAAK4EgVD7AOoO21gT/uWc6z02dSaQ6RryxDSGFMQGW0ro6oKGhjYaGNi7+9QMMGtyD2/97Jh+9+w1Nje387U/PFpgBpQK5vA0vFvLD8Y1GJttTJn+szPU0IG2LfQ7eYhXMPECAAB0RCK8A6wQ+/3A+5xzzNxLtqdyHIQfteli2QCuF1tKEu0tTNVn4EYBZEdNB2Myf38DVF/yLG+78GQA9+9Vw1XmP5BKQtTnXbk4gmhMF52ZrfhURYBoQluTCqw9h8NBe33vuAQIE6IzAbBhgrcfyJc38+ohbCgUXGLtcyEE5IcaOH4KyJdqRRrBIgbYE2pEoS5SkMPz0owW0tRrBtMMuY7npgV+w+fbrg+Xnezlmf9fpfE/n4u91TtihNULAVbccy2S/0GWAAAFWPQLhFWCtx18vfww37ZU8rrXmgw8XmEhDUeRlyVwUYkcIwf1/fyX7dr1R/bjyT0fy8LNns9G4wQhbosOlBJjqIMQgGnW4+aGT2XTiiO836QABApRFYDYMsFYj0Z7irRc/KdtGC1DhIonE+W0sUWhCzEPD8tbs/9taE9x1+6s89fh7xNtTCCEQjo2WEuF6aFcBvoaVkWaeJhoLsfshW3LCWbsjZbAnDBBgdSMQXgHWaqxY2ozyyudkaac7ycR+NnGRrjadOByAtrYkp//yTuZ9s9wkQQth+ga0p5BKI7WX9YllgkX2P24bTvjN7kFYfIAAaxCB8AqwViNWFelGq66jDItCa6QQTNlrUwAeuOeNnODKbwbG9FgdYUCshoVfLwVgownDOPjE7dlyhzErd/0AAQKsNALhFWCtRm2PSjbZcgSz3/6qdCPVBRtGBvkyyQ+yOP6UKQB4nuK/U98tW2JFa9jlqMnsd+DmSClwQsHPJ0CAHwqBcT7AWo+fnjKlrFWwR20M2y7zKGtt/F35UYGeQknBlL3HAcbX1dKSKN0HIKVg/rfLCUecQHAFCPADIxBeAdZ6bLzFcC76y1FUFDEhbrLlCG5/9ixO+80epTvwc7+UFChAWRIVCxGqjFBZFQUgHHG6k+NMNBZayVkECBBgVSLYPgZYJ7DVThty32sX8Ma0j1gwdynRijBb/2QsfQfWAbD73uOprIrwlz8+zYq86MEshIA8bUlK2GjsQF5+7iM2GDuIgYN7sOWkkcx488uSpkPPU2y/04arZX4BAgT4bgiKUZZBUIxy3cTSJc3UL2rk1Rc/5dFHOpQk8WuA0UFAbb7Veuxz8JZcfP7DaKU78fFKKdh4/BCuveHIsrRQAQL8mLEm18xA8wrwP4fefarp3aeasZsMpv/AOu698zWaGttzZUyK4N0ZX7FwfgPnXLgvN1z7JO1tKWxbmlM8xaabD+fCy/YPBFeAAGsJAuEV4H8WQgj2P3gL9t5/Mz79eCHvvPkl993+StG2ytMsWtDAiiXNPPjYGbz8wsfM/Wop4YjD1tuuz6jR/dfw6AMECFAOgfAK8D8P27YYu8lgHrrrNaQUJX1aWmue/s97HHTEJHbdY9waHmWAAAG+C4JowwA/Gixb0lI2jwugYUWRYI8AAQKsdQiEV4AfDXr3rUaWIujFBCT27B0E5gQIsC5gtQivlpYWzjjjDIYOHUo0GmXy5MnMmGGivtLpNOeccw4bb7wxFRUVDBgwgKOPPpqFCxcW9DFs2DBDipr3uvrqqwvazJ49m2233ZZIJMLgwYO55pprOo3l4YcfZsyYMUQiETbeeGOefPLJ1THlAOsAdtt70y41r9332XQNjSZAgADfB6tFeJ144olMmzaNu+++mw8++IBddtmFKVOmsGDBAtrb23n33Xe58MILeffdd/n3v//NZ599xj777NOpn8suu4xFixZlX6eeemr2WHNzM7vssgtDhw5l5syZXHvttVxyySXcdttt2TZvvPEGhx9+OCeccALvvfce++23H/vttx8ffvjh6ph2gLUcW249ik23GF5U+5JSMGR4b3bbOxBeAQKsC1jleV7xeJyqqioee+wx9txzz+znEyZMYPfdd+eKK67odM6MGTPYcsst+eabbxgyZAhgNK8zzjiDM844o+h1br75Zi644ALq6+sJhQzrwbnnnsvUqVP59NNPATj00ENpa2vjiSeeyJ631VZbMX78eG655ZYu5xLkef3vIZlIc+uNz/L047OyNcKEFGyzwxhOP2dPqmtiP/AIAwRYd7FO53m5rovneUQihVQ+0WiU1157reg5TU1NCCGora0t+Pzqq6/m8ssvZ8iQIRxxxBH86le/wrbNkKdPn852222XFVwAu+66K7///e9paGigrq6O6dOnc+aZZxb0ueuuuzJ16tTvP9EA6yTCEYfTzt6TY3++Ex/N/halNKM3GECvPsHmJECAdQmrXHhVVVUxadIkLr/8cjbYYAP69u3L/fffz/Tp0xk5cmSn9olEgnPOOYfDDz+8QFKfdtppbLbZZvTo0YM33niD8847j0WLFvHHP/4RgPr6eoYPH17QV9++fbPH6urqqK+vz36W36a+vr7o2JPJJMlkMvu+ubl55W5CgLUe1TVRJm07+oceRoAAAVYSqyXP6+677+b4449n4MCBWJbFZpttxuGHH87MmTML2qXTaQ455BC01tx8880Fx/I1pk022YRQKMTPf/5zrrrqKsLh8OoYNldddRWXXnrpauk7QIAAAQKsOqyWgI311luPl19+mdbWVubNm8fbb79NOp1mxIgR2TYZwfXNN98wbdq0Lu2jEydOxHVd5s6dC0C/fv1YvHhxQZvM+379+pVtkzneEeeddx5NTU3Z17x5877TvAMECBAgwJrBas3zqqiooH///jQ0NPDMM8+w7777AjnBNWfOHJ577jl69uzZZV+zZs1CSkmfPn0AmDRpEq+88grpdDrbZtq0aYwePZq6urpsm+eff76gn2nTpjFp0qSi1wiHw1RXVxe8AgQIECDAWgi9GvD000/rp556Sn/11Vf62Wef1ePGjdMTJ07UqVRKp1Ipvc8+++hBgwbpWbNm6UWLFmVfyWRSa631G2+8oa+//no9a9Ys/eWXX+p77rlH9+7dWx999NHZazQ2Nuq+ffvqo446Sn/44Yf6gQce0LFYTN96663ZNq+//rq2bVv/4Q9/0J988om++OKLteM4+oMPPujWPJqamjSgm5qaVu0NChAgQID/QazJNXO1CK8HH3xQjxgxQodCId2vXz998skn68bGRq211l9//bXGFGTv9HrxxRe11lrPnDlTT5w4UdfU1OhIJKI32GAD/bvf/U4nEomC67z//vt6m2220eFwWA8cOFBfffXVncby0EMP6fXXX1+HQiG90UYb6f/+97/dnkcgvAIECBCg+1iTa2ZQz6sMgjyvAAECBOg+1uSaGXAbBggQIECAdQ6B8AoQIECAAOscAuEVIECAAAHWOQTFKMsg4w4MmDYCBAgQoGtk1so1EUoRCK8yaGlpAWDw4ME/8EgCBAgQYN1BS0sLNTU1q/UaQbRhGSilWLhwIVVVVQhRuojhmkRzczODBw9m3rx562QE5Lo8/nV57BCM/4fGj2H8WmtaWloYMGAAUq5er1SgeZWBlJJBgwb90MMoinWdAWRdHv+6PHYIxv9D4399/Ktb48ogCNgIECBAgADrHALhFSBAgAAB1jkEwmsdQzgc5uKLL15tZWFWN9bl8a/LY4dg/D80gvGvWgQBGwECBAgQYJ1DoHkFCBAgQIB1DoHwChAgQIAA6xwC4RUgQIAAAdY5BMIrQIAAAQKscwiE1xpAS0sLZ5xxBkOHDiUajTJ58mRmzJiRPX7ssccihCh47bbbbgV9rFixgiOPPJLq6mpqa2s54YQTaG1tLWgze/Zstt12WyKRCIMHD+aaa67pNJaHH36YMWPGEIlE2HjjjXnyySe/19g7jjvzuvbaa7Nthg0b1un41VdfvVrG/sorr7D33nszYMAAhBBMnTq14LjWmosuuoj+/fsTjUaZMmUKc+bMKWizpu51x7FMmDCBnXbaaaXHPnfuXE444QSGDx9ONBplvfXW4+KLLyaVShW0KfZ9vfnmm99r7FOmTOG+++773vd+TT0rq2P8L730UsnfQ+Y380Pe/3//+9/ssssu9OzZEyEEs2bN6nTfEokEJ598Mj179qSyspIDDzyQxYsXF7T59ttv2XPPPYnFYvTp04ezzjoL13UL2rz00ktsttlmhMNhRo4cyR133NHpWn/9618ZNmwYkUiEiRMn8vbbb3dqUxarvdxlAH3IIYfoDTfcUL/88st6zpw5+uKLL9bV1dV6/vz5WmutjznmGL3bbrvpRYsWZV8rVqwo6GO33XbT48aN02+++aZ+9dVX9ciRI/Xhhx+ePd7U1KT79u2rjzzySP3hhx/q+++/X0ejUX3rrbdm27z++uvasix9zTXX6I8//lj/9re/1Y7j6A8++GClx54/5kWLFunbb79dCyH0l19+me1j6NCh+rLLLito19raulrG/uSTT+oLLrhA//vf/9aAfvTRRwuOX3311bqmpkZPnTpVv//++3qfffbRw4cP1/F4fI3f645jyVQPf+CBB1Zq7E899ZQ+9thj9TPPPKO//PJL/dhjj+k+ffroX//619k+MpXMn3vuuYLvI5VKfa+x77PPPrpv3776nHPO+V73fk09K6tj/MlkstPv4cQTT9TDhw/XSqkf/P7f9f/tnWtMU2cYx/+2cpBOu4IFqkYIoGN4tyhd1SxzEMGY2GmiGzFqvKBOk31wm0aNMWqcRrx8IJtRI0VdJmNZomYoDlG3GQsqKd6oJN6mqHVJRwWjjtt/H0xPOLRIAVvX5f0lJJz3POc5vz5926c9vC2HDnHjxo3cv38/AdBut7M9y5Yt4+DBg1lWVsbLly/zgw8+4IQJE+T9zc3NHDFiBDMyMmi323nixAnq9XquWbNGjrlz5w41Gg1XrlzJ6upq5uXlUa1Ws6SkRI4pLCykJEnMz8/njRs3mJOTQ51OxydPnng5dYRoXgHm+fPnVKvV/OWXXxTjRqOR69atI/mqeVkslg5zVFdXEwAvXbokj508eZK9evXiw4cPSZLfffcdIyMj+c8//8gxq1evZnJysrw9e/ZsTps2TZHbZDJx6dKl3XZvj8Vi4ccff6wYi4+P5+7duzu8fYFwJ+n1AG5tbaXBYGBubq485na7GR4eziNHjpAMXq07c+mOuy+2b9/OhIQEedvz5OnrietNuZPdqz0ZnLkSSP+2NDY2Mjo6mps2bZLH3lb929KRg9vtZlhYGH/66Sd5zOFwEABtNhvJVy8OVSoVnU6nHLNnzx5qtVr5Plm1ahWHDx+uyP3pp58yMzNT3k5LS+OKFSvk7ZaWFg4cOJBbt27tsC7tEZcNA0xzczNaWlrQp08fxXhERATOnz8vb587dw4xMTFITk7G559/DpfLJe+z2WzQ6XQYN26cPJaRkQGVSoWKigo55sMPP4QkSXJMZmYmampqUFdXJ8dkZGQoPDIzM2Gz2Xrk7uHJkycoLi7GokWLvPZt27YN/fv3x9ixY5Gbm6u4zBAId1/cvXsXTqdTkefdd9+FyWSS8wSr1v64dNXdF0+fPkVUVJTX+PTp0xETE4NJkybh+PHjin1v2r2rxwR6rgTa38Px48fhcrmwYMECr33Brr8/VFZWoqmpSZH3/fffR1xcnOLxMXLkSMTGxirc6uvrcePGDb/8GxsbUVlZqYhRqVTIyMjokr/4Yt4A069fP5jNZmzevBkpKSmIjY3FkSNHYLPZMGTIEABAVlYWZs6ciYSEBNy+fRtr167F1KlTYbPZoFar4XQ6ERMTo8jbu3dvREVFwel0AgCcTicSEhIUMZ4J5nQ6ERkZCafTqZh0nhhPju64t+XgwYPo168fZs6cqRj/4osvYDQaERUVhQsXLmDNmjV4/Pgxdu3aFTB3X3hiX5cnWLX2x6Wr7u25desW8vLysGPHDnmsb9++2LlzJyZOnAiVSoWff/4Zn3zyCY4ePYrp06fL53qT7l05JhhzJZD+bTlw4AAyMzMVX+79turvD06nE5IkQafTvfbcvs7b1qujmPr6erx48QJ1dXVoaWnxGXPz5k2/fUXzCgKHDx/GwoULMWjQIKjVahiNRmRnZ6OyshIA8Nlnn8mxI0eOxKhRo5CUlIRz584hPT39bWkD6Ny9Lfn5+ZgzZ47XO7WVK1fKv48aNQqSJGHp0qXYunXrf+arZv5vPHz4EFlZWZg1axZycnLkcb1er7g/xo8fj0ePHiE3N1d+8nyb/F/mSm1tLU6dOoWioiLF+H+9/qGEuGwYBJKSkvDbb7/h2bNnePDgAS5evIimpiYkJib6jE9MTIRer8etW7cAAAaDAX/99Zciprm5GX///TcMBoMc035VkGe7sxjP/p64//HHH6ipqcHixYs7KwdMJhOam5tx7969gLq3xxP7ujzBqrU/Ll119/Do0SNMnjwZEyZMwL59+7xytcdkMslzLRDu3T3G4/am50ow/K1WK/r37+9XQwpG/f3BYDCgsbERbrf7tefubv21Wi0iIiKg1+uhVqt77C+aVxB55513MGDAANTV1eHUqVOwWCw+42pra+FyuTBgwAAAgNlshtvtVrzbOXPmDFpbW2EymeSY33//HU1NTXJMaWkpkpOTERkZKceUlZUpzlVaWgqz2dxj9wMHDiA1NRWjR4/uNFdVVRVUKpV8eS7Q7h4SEhJgMBgUeerr61FRUSHnCVat/XHpqjvw6h3XRx99hNTUVFitVr/+IWBVVZU81wLh3t1jPG5veq4E2p8krFYr5s2bh7CwsA5vW9vbGOj6+0NqairCwsIUeWtqanD//n3F4+PatWuKF3ilpaXQarUYNmyYX/6SJCE1NVUR09rairKysq75+720Q9BtSkpKePLkSd65c4e//vorR48eTZPJxMbGRjY0NPCrr76izWbj3bt3efr0aRqNRg4dOpQvX76Uc2RlZXHs2LGsqKjg+fPnOXToUMXybbfbzdjYWM6dO5fXr19nYWEhNRqN1xLi3r17c8eOHXQ4HNywYUOnS+Vf5+7h6dOn1Gg03LNnj9fxFy5c4O7du1lVVcXbt2/z+++/Z3R0NOfNmxcQ94aGBtrtdtrtdgLgrl27aLfb+eeff5J8tcRYp9Px2LFjvHr1Ki0Wi8+l8sGodXuXadOmceDAgSwvL++We21tLYcMGcL09HTW1tYqlmJ7KCgo4A8//ECHw0GHw8EtW7ZQpVIxPz+/R+4Wi4Xx8fEsLy/vdu2DOVcC4e/h9OnTBECHw8H2vM36u1wu2u12FhcXEwALCwtpt9sV82PZsmWMi4vjmTNnePnyZZrNZprNZnm/Z6n8lClTWFVVxZKSEkZHR/tcKv/111/T4XDw22+/9blUPjw8nAUFBayuruaSJUuo0+kUqxg7QzSvIPDjjz8yMTGRkiTRYDBwxYoVdLvdJF8tR58yZQqjo6MZFhbG+Ph45uTkeN2JLpeL2dnZ7Nu3L7VaLRcsWMCGhgZFzJUrVzhp0iSGh4dz0KBB3LZtm5dLUVER33vvPUqSxOHDh7O4uLjb7h727t3LiIgIr3GSrKyslD+/1KdPH6akpPCbb75RNOY36X727FkC8PqZP38+yVfLjNevX8/Y2FiGh4czPT2dNTU1ihzBqnV7F6PR2CN3q9Xq8/i2r1ELCgqYkpJCjUZDrVbLtLQ0xdLo7rqnp6fz0KFDPfIP5lwJhL+H7OxsxWej2vI269/R/NiwYYOc98WLF1y+fDkjIyOp0Wg4Y8YMRXMjyXv37nHq1KmMiIigXq/nl19+yaamJkXM2bNnOWbMGEqSxMTERFqtVq/bmJeXx7i4OEqSxLS0NJaXl/usWUeIf4kiEAgEgpBD/M1LIBAIBCGHaF4CgUAgCDlE8xIIBAJByCGal0AgEAhCDtG8BAKBQBByiOYlEAgEgpBDNC+BQCAQhByieQkEAoEg5BDNSyAQCAQhh2heAoFAIAg5RPMSCAQCQcghmpdAIBAIQo5/AdfdXrgN+pF6AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H8Sy0Zb3_Q2N"
      },
      "source": [
        "# OLS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9CnhsxjW2K4O"
      },
      "source": [
        "Statsmodels es la librería de operaciones estadísticas que usaremos para estimar OLS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qoIeLvSTkMed"
      },
      "source": [
        "import statsmodels.api as sm"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QJmhtgjqoUW2"
      },
      "source": [
        "Nuestro modelo a explorar es el siguiente:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B5QbC7SGocxS"
      },
      "source": [
        "$price_i=\\beta_0+ \\beta_1*bedrooms_i+\\epsilon_i$"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.formula.api as smf"
      ],
      "metadata": {
        "id": "NbfVx1gXekv2"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "modelo=smf.ols('price ~ bedrooms', data=departamentos)"
      ],
      "metadata": {
        "id": "uBNMQno5eyG1"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "resultados=modelo.fit()"
      ],
      "metadata": {
        "id": "XxSD9T2jfUnE"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(resultados.summary())"
      ],
      "metadata": {
        "id": "xRNB3xJ2fRt-",
        "outputId": "0cf50ae7-3ed7-463e-e4c1-e005dbbfa8b4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  price   R-squared:                       0.049\n",
            "Model:                            OLS   Adj. R-squared:                  0.049\n",
            "Method:                 Least Squares   F-statistic:                     1443.\n",
            "Date:                Thu, 13 Jun 2024   Prob (F-statistic):          7.68e-308\n",
            "Time:                        21:29:04   Log-Likelihood:            -3.0876e+05\n",
            "No. Observations:               27879   AIC:                         6.175e+05\n",
            "Df Residuals:                   27877   BIC:                         6.175e+05\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept   1.151e+04    205.145     56.088      0.000    1.11e+04    1.19e+04\n",
            "bedrooms    4253.5067    111.984     37.983      0.000    4034.013    4473.000\n",
            "==============================================================================\n",
            "Omnibus:                    39834.368   Durbin-Watson:                   1.851\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         59814269.062\n",
            "Skew:                           7.918   Prob(JB):                         0.00\n",
            "Kurtosis:                     229.365   Cond. No.                         5.02\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qeYPWdOaAwjD"
      },
      "source": [
        "Nuestro resultado es:\n",
        "precio= 11500+ 4253* bedrooms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MG4B4_iFrJ5U"
      },
      "source": [
        "## Graficando"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hqy13i7ZtXZV"
      },
      "source": [
        "El (geo)dataframe tiene algunos metodos que permiten graficar directamente"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "departamentos=departamentos.dropna(subset=['bedrooms','price'])"
      ],
      "metadata": {
        "id": "Pp-atMPYg1Za"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uRsjNu2G9lMJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "3954be9d-f0db-487b-d28f-7e3b7ddab250"
      },
      "source": [
        "departamentos.plot.scatter(x='bedrooms',  y='price')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='bedrooms', ylabel='price'>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "243MRDB1-eBb"
      },
      "source": [
        "Scatter plot\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QmSsnwG1-Ei6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "5fa8d4fa-9871-43a7-c49c-304936857ae0"
      },
      "source": [
        "departamentos.loc[departamentos['bedrooms']>0].plot.scatter(x='bedrooms',  y='price')"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='bedrooms', ylabel='price'>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMA3JZMStfKk"
      },
      "source": [
        "Para hacer otros graficos más complejos vamos a usar otras librerias como matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n_dgi2pDAVbS"
      },
      "source": [
        "ypred = resultados.predict(departamentos['bedrooms'])"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h9DvHruYAqij",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "d8383a4e-bad0-4d90-fbd7-f8763619fe56"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(departamentos['bedrooms'], departamentos['price'], 'o', label=\"Data\")\n",
        "ax.plot(departamentos['bedrooms'], ypred, 'b-', label=\"Prediction\")\n",
        "#ax.plot(np.hstack((x1, x1n)), np.hstack((ypred, ynewpred)), 'r', label=\"OLS prediction\")\n",
        "ax.legend(loc=\"best\");"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hvCFmNhhXyHe"
      },
      "source": [
        "# Ejemplo de ploteo con layers (capas)\n",
        "Vamos a utilizar la capa de límites de partidos y departamentos del IGN.\n",
        "Ver más aqui:\n",
        "https://datos.gob.ar/dataset/ign-unidades-territoriales"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3aSSL0n_Npup"
      },
      "source": [
        "limites=gpd.GeoDataFrame.from_file(\"https://github.com/rpasquini/urban-econometrics/blob/master/data/ign_departamento.zip?raw=true\")\n",
        "#En caso de tener el zip en el espacio virtual del colab: limites=gpd.read_file(\"zip:///content/ign_departamento.zip\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qGtwtXfLRobK"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10, 10))\n",
        "limites.to_crs(\"EPSG:4326\").plot(ax=ax)\n",
        "departamentos.to_crs(\"EPSG:4326\").plot(ax=ax,color='orange')\n",
        "ax.set_xlim(-59, -58)\n",
        "ax.set_ylim(-35, -34.1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wcUhgJbw4j2t"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10, 10))\n",
        "limites.to_crs(\"EPSG:4326\").plot(ax=ax)\n",
        "departamentos.loc[departamentos.price<80000].to_crs(\"EPSG:4326\").plot(ax=ax,column='price')\n",
        "ax.set_xlim(-59, -58)\n",
        "ax.set_ylim(-35, -34.1)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}